System and method for enabling image recognition and searching of remote content on display

ABSTRACT

Images are analyzed by programmatic mechanisms for assessing one or more remote web pages to retrieve content on display at remote web pages. The retrieved images may be analyzed to determine information about an object shown in a corresponding images of the content on display. At least a portion of the object shown in the corresponding image of the content on display may be made selectable and associated with the determined information. This determined information may subsequently be used, in for example, search applications.

RELATED APPLICATIONS

This application also claims benefit of priority to U.S. ProvisionalPatent Application No. 60/864,781, filed Nov. 7, 2006, entitled AN IMAGESIMILARITY SYSTEM, the aforementioned priority application being herebyincorporated by reference in its entirety.

This application also claims benefit of priority to U.S. ProvisionalPatent Application No. 60/909,414, filed Mar. 30, 2007, entitledTECHNIQUE FOR ACTIVATING IMAGES ON WEBSITES IN ORDER TO ENABLE VISUALSEARCH OF IMAGE PORTIONS BY WEBSITE USERS, the aforementioned priorityapplication being hereby incorporated by reference in its entirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 11/246,742, entitled SYSTEM AND METHOD FOR ENABLING THE USE OFCAPTURED IMAGES THROUGH RECOGNITION, filed on Oct. 7, 2005 now U.S. Pat.No. 7,519,200; which claims benefit of priority to U.S. ProvisionalPatent Application No. 60/679,591, entitled METHOD FOR TAGGING IMAGES,filed May 9, 2005; both of the aforementioned priority applicationsbeing hereby incorporated by reference in their entirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 11/246,741, entitled SYSTEM AND METHOD FOR ENABLING SEARCH ANDRETRIEVAL FROM IMAGE FILES BASED ON RECOGNIZED INFORMATION, filed Oct.7, 2005 now U.S. Pat. No. 7,809,722; which claims benefit of priority toU.S. Provisional Patent Application No. 60/679,591, entitled METHOD FORTAGGING IMAGES, filed May 9, 2005; both of the aforementioned priorityapplications being hereby incorporated by reference in their entirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 11/246,589, entitled SYSTEM AND METHOD FOR RECOGNIZING OBJECTSFROM IMAGES AND IDENTIFYING RELEVANCY AMONGST IMAGES AND INFORMATION,filed on Oct. 7, 2005 now U.S. Pat. No. 7,809,192; which claims benefitof priority to U.S. Provisional Patent Application No. 60/679,591,entitled METHOD FOR TAGGING IMAGES, filed May 9, 2005; both of theaforementioned priority applications being hereby incorporated byreference in their entirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 11/246,434, entitled SYSTEM AND METHOD FOR PROVIDINGOBJECTIFIED IMAGE RENDERINGS USING RECOGNITION INFORMATION FROM IMAGES,filed on Oct. 7, 2005 now U.S. Pat. No. 7,783,135; which claims benefitof priority to U.S. Provisional Patent Application No. 60/679,591,entitled METHOD FOR TAGGING IMAGES, filed May 9, 2005; both of theaforementioned priority applications being hereby incorporated byreference in their entirety.

TECHNICAL FIELD

The disclosed embodiments relate generally to the field of digital imageprocessing.

BACKGROUND

Digital photography has become a consumer application of greatsignificance. It has afforded individuals convenience in capturing andsharing digital images. Devices that capture digital images have becomelow-cost, and the ability to send pictures from one location to theother has been one of the driving forces in the drive for more networkbandwidth.

Due to the relative low cost of memory and the availability of devicesand platforms from which digital images can be viewed, the averageconsumer maintains most digital images on computer-readable mediums,such as hard drives, CD-Roms, and flash memory. The use of file foldersare the primary source of organization, although applications have beencreated to aid users in organizing and viewing digital images. Somesearch engines, such as GOOGLE, also enables users to search for images,primarily by matching text-based search input to text metadata orcontent associated with images.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for analyzing images of objects, under anembodiment of the invention.

FIG. 2 illustrates a category-mapping system for enabling humanoperators to facilitate a system such as described in FIG. 1, accordingto an embodiment of the invention.

FIG. 3 illustrates implementation of the editor interface, under anembodiment of the invention.

FIG. 4A illustrates a manual enrichment process, according to anembodiment of the invention.

FIG. 4B illustrates a manual enrichment process performed on results ofsegmentation process, such as performed by an image segmentizercomponent of another embodiment described herein, according to one ormore embodiments of the invention.

FIG. 5 illustrates a method in which image segmentation and alignmentmay be performed using a statistical analysis, according to one or moreembodiments described herein.

FIG. 6A-6C illustrates results of an embodiment for segmentation andalignment, as applied to an image of a shoe, under an embodiment of theinvention.

FIG. 7 illustrates a feature extraction module, under an embodiment ofthe invention.

FIG. 8 illustrates modules of a feature extraction system or module,under an embodiment of the invention.

FIG. 9 illustrates components that comprise the global feature module,according to an embodiment of the invention.

FIG. 10 illustrates a method for determining and extracting localfeatures from an image object, under an embodiment of the invention.

FIG. 11 illustrates a search system for enabling search of images,according to an embodiment of the invention.

FIG. 12 illustrates a method for implementing a search system, such asdescribed with an embodiment of FIG. 11, according to one or moreembodiments of the invention.

FIG. 13 illustrates a front-end system for use with an image searchsystem such as shown and described in FIG. 1, under an embodiment of theinvention.

FIG. 14 illustrates a technique for enabling and performing a similaritysearch using an image input, according to one or more embodiments of theinvention.

FIG. 15A illustrates an implementation of a text search performed toview images of merchandise items, under an embodiment of the invention.

FIG. 15B illustrates results of a similarity search for the samemerchandise item, under an embodiment of the invention.

FIG. 16 illustrates a slider feature for a user-interface, according toan embodiment of the invention.

FIG. 17 illustrates a color selector feature for a user-interface,according to an embodiment of the invention.

FIG. 18 illustrates implementation of a selector graphic feature forenabling a user to select a portion of an image of an object, accordingto an embodiment of the invention.

FIG. 19 shows an example of how image features may be combined in aquery, under an embodiment of the invention.

FIG. 20 illustrates an example of a result that may be achieved througha user-interface feature, according to an embodiment of the invention.

FIG. 21 illustrates a method for implementing an e-commerce system usingany combination of embodiments described herein, according to anotherembodiment of the invention.

FIG. 22 illustrates a record corresponding to a processed content itemhaving data items that are determined or used in accordance with one ormore embodiments described herein

FIG. 23 illustrates a method for using remote web content for purpose ofidentifying search criteria for performing an image search orcombination search, according to one or more embodiments of theinvention.

FIG. 24 illustrates a back end process for activating images on a webpage, under an embodiment of the invention.

DETAILED DESCRIPTION

Numerous embodiments are described herein for the use of digital imagesand photography. Embodiments described herein enable programmaticdetection and/or identification of various types and classes of objectsfrom images, including objects that are items of commerce ormerchandise. Among the numerous embodiments described herein,embodiments include (i) systems and methods for detecting and analyzingimages; (i) systems and methods searching for images using image data,text data, features, and non-textual data; (iii) user-interface andfeatures thereof for enabling various forms of search on a collection ordatabase of analyzed images; (iv) e-commerce applications for enablingvisual, non-textual and visually aided searches of merchandise items;and (v) retrieval and analysis of images from third-part y sites andnetwork locations. Embodiments described herein further includecomponents, modules, and sub-processes that comprise aspects or portionsof other embodiments described herein.

One or more embodiments described herein provide for a system forcreating a data collection of recognized images. The system includes animage analysis module that is configured to programmatically analyzeindividual images in a collection of images in order to determineinformation about each image in the collection. The system may alsoinclude a manual interface that is configured to (i) interface with oneor more human editors, and (ii) displays a plurality of panelsconcurrently. Individual panels may be provided for one or more analyzedimages, and individual panels may be configured to display informationthat is at least indicative of the one or more images of that paneland/or of the information determined from the one or more images.Additionally, the manual interface enables the one or more human editorsto view the plurality of panels concurrently and to interact with eachof the plurality of panels in order to correct or remove any informationthat is incorrectly determined from the image of that panel.

One or more embodiments enable image analysis of content items thatinclude image. Among other applications, the analysis of such contentitems (including images or images with text and/or metadata) enables theuse of content or image based searching. In one embodiment, a searchquery may be derived from image data, or values for image data.

As used herein, the term “image data” is intended to mean data thatcorresponds to or is based on discrete portions of a captured image. Forexample, with digital images, such as those provided in a JPEG format,the image data may correspond to data or information about pixels thatform the image, or data or information determined from pixels of theimage. Another example of “image data” is signature or other non-textualdata that represents a classification or identity of an object, as wellas a global or local feature.

The terms “recognize”, or “recognition”, or variants thereof, in thecontext of an image or image data (e.g. “recognize an image”) is meantto means that a determination is made as to what the image correlatesto, represents, identifies, means, and/or a context provided by theimage. Recognition does not mean a determination of identity by name,unless stated so expressly, as name identification may require anadditional step of correlation.

As used herein, the terms “programmatic”, “programmatically” orvariations thereof mean through execution of code, programming or otherlogic. A programmatic action may be performed with software, firmware orhardware, and generally without user-intervention, albeit notnecessarily automatically, as the action may be manually triggered.

One or more embodiments described herein may be implemented usingprogrammatic elements, often referred to as modules or components,although other names may be used. Such programmatic elements may includea program, a subroutine, a portion of a program, or a software componentor a hardware component capable of performing one or more stated tasksor functions. As used herein, a module or component, can exist on ahardware component independently of other modules/components or amodule/component can be a shared element or process of othermodules/components, programs or machines. A module or component mayreside on one machine, such as on a client or on a server, or amodule/component may be distributed amongst multiple machines, such ason multiple clients or server machines. Any system described may beimplemented in whole or in part on a server, or as part of a networkservice. Alternatively, a system such as described herein may beimplemented on a local computer or terminal, in whole or in part. Ineither case, implementation of system provided for in this applicationmay require use of memory, processors and network resources (includingdata ports, and signal lines (optical, electrical etc.), unless statedotherwise.

Embodiments described herein generally require the use of computers,including processing and memory resources. For example, systemsdescribed herein may be implemented on a server or network service. Suchservers may connect and be used by users over networks such as theInternet, or by a combination of networks, such as cellular networks andthe Internet. Alternatively, one or more embodiments described hereinmay be implemented locally, in whole or in part, on computing machinessuch as desktops, cellular phones, personal digital assistances orlaptop computers. Thus, memory, processing and network resources may allbe used in connection with the establishment, use or performance of anyembodiment described herein (including with the performance of anymethod or with the implementation of any system).

Furthermore, one or more embodiments described herein may be implementedthrough the use of instructions that are executable by one or moreprocessors. These instructions may be carried on a computer-readablemedium. Machines shown in figures below provide examples of processingresources and computer-readable mediums on which instructions forimplementing embodiments of the invention can be carried and/orexecuted. In particular, the numerous machines shown with embodiments ofthe invention include processor(s) and various forms of memory forholding data and instructions. Examples of computer-readable mediumsinclude permanent memory storage devices, such as hard drives onpersonal computers or servers. Other examples of computer storagemediums include portable storage units, such as CD or DVD units, flashmemory (such as carried on many cell phones and personal digitalassistants (PDAs)), and magnetic memory. Computers, terminals, networkenabled devices (e.g. mobile devices such as cell phones) are allexamples of machines and devices that utilize processors, memory, andinstructions stored on computer-readable mediums.

System for Analyzing Content Items Carrying Images

FIG. 1 illustrates a system for analyzing images of objects, under anembodiment of the invention. A system such as shown and described by anembodiment of FIG. 1 may include applications such as enabling searchand retrieval, and/or enabling display of programmatically determinedinformation. As described with other embodiments herein, a system suchas described with an embodiment of FIG. 1 may be used to enable ane-commerce system that enables use of image analysis, includingimage-to-image searching.

In an embodiment, a system 100 is provided for analyzing content itemsthat carry images. The system includes modules in the form ofprocurement 105, image segmentizer 110, feature extraction 120, andanalysis data generation 135. According to embodiments, system 100 mayoperate on content items 102 that include images, including records orweb content that package images along with text and/or metadata.Specific examples of content items 102 for use with embodimentsdescribed herein include web content, such as provided for merchandise,or web pages (e.g. e-commerce sites, blogs) on which people ormerchandise are displayed. Other content items include images that maybe uploaded by persons.

In performing various analysis operations, system 100 may determineand/or use information that is descriptive or identifiable to objectsshown in the images of the content items. Accordingly, system 100 mayanalyze content items 102 by (i) recognizing or otherwise determininginformation about an object contained in an image of the procuredcontent item, through an analysis of image data, text data, metadata orany combination thereof, and/or (ii) recognizing or otherwisedetermining information about an object using existing or knowninformation from a source other than the content item. The informationabout the object contained in the image may correspond to one or moreclassifications (e.g. “Men's apparel”, “clothing”, sunglasses”),determination of type (e.g. manufacturer or brand identification),attribute information (color, pattern, shape), and/or information thatis sufficiently specific to identify the object (such as for purchase).In order to programmatically determine information about the objectcontained in the image of a given content item 102, one or moreembodiments may employ object determinator 140, which may determine theinformation about the object(s) in the image of a given content item 102using image analysis and recognition, text analysis, metadata analysis,human input, or a combination thereof. In this way, the objectdeterminator 140 may use both information determined from the source,and existing or known information from a source other than the contentitem.

One or more embodiments also contemplate use of a manual enrichment toenhance the accuracy of individual components and/or the system 100 as awhole. Accordingly, an embodiment includes an editor interface 160 forenabling manual confirmation of programmatically determined information,as well as manual editing or correction. FIG. 4A and FIG. 4B illustratehow manual enrichment and/or editor interface 160 may operate, accordingto one or more embodiments of the invention.

More generally, in one embodiment, system 100 handles images that aregenerated independently of the system, so that the system has limitedadvance knowledge about what the contents of the images are, or whatform or formatting is used in conveying objects shown in the images(e.g. alignment, image formatting). Alternatively, the system mayoperate on a collection or library where some knowledge of the contentsof the images is known in advance. This knowledge may be provided by,for example, human operators. For example, specific online merchants maybe known to sell products of a specific category, or products at aparticular site may be known to be of a particular classification whendisplayed under a specific tab. Human operators may provide thisinformation in advance.

Procurement 105 may perform anyone of many processes to procure contentitems 102 that contain unprocessed images. In one implementation,procurement 105 may crawl network locations to locate web files,including files that contain images. In another implementation,procurement 105 may interface or receive feeds from a library ofcollection of content item 102. Still further, procurement 105 mayreceive triggers to access other sites or network locations, and/orhandle uploads or content item submissions from users.

Content items 102 may include files or portions thereof that containimages (exclusively, or in combination with other data). In oneembodiment, content items 102 correspond to image files, combination oftext/image files, or to portions of records or pages (e.g. web pages)that contains images (and possibly text). Content items 102 may alsocontain metadata, which includes tags or other data that may notnecessarily form part of the displayed content of the content item. Theoutput of procurement 105 includes images 104, and possibly pertinenttext.

Embodiments provide that system 100 attempts to determine informationabout objects identified in content items 102 before or independent ofimage recognition/analysis processes are performed. In one embodiment,procurement 105 extracts text and metadata 103 from content item 102.The text and metadata 103 are forwarded to the object determinator 140.A text and metadata analyzer 145 may determine an object identifier 143for an object contained in the content item. The object identifier 143may correspond to an identification of a class or type, although otherinformation may also be determined. In one embodiment, the text andmetadata analyzer 145 portion of the object determinator 140 determinesas much information as possible from the text and metadata 103. Forexample, in the case where content item 102 includes a web-based recordof a shoe (e.g. as an item for merchandise), the text and metadataanalyzer 145 may use the text and metadata to determine various levelsof classification about the shoe, including whether it is for men orwomen, the type of shoe (sneakers, dress shoes, sandals), its coloring,price range, manufacturer, its quality (e.g. sample ratings provided ona website where the shoe is provided for sale), the material used tomanufacture the shoe, and various other information. When such detailedinformation is not available, the text and metadata analyzer 145 may usethe text and metadata 103 to perform an analysis on the content item102, in order to identify hints or clues as to the object of the contentitem 102. Such analysis may correspond to, for example, key wordidentification of the text of the content item, as well as metadataassociated with the source of the content item 102. The metadata maycorrespond to the domain, or a category, specification or attributeassociated with the content item. FIG. 2 illustrates a more detaileddescription of how the text and metadata analyzer 145 of the objectdeterminator 140 may be used, under one or more embodiments of theinvention. In another embodiment, the object image data analysis 146 canbe used in conjunction with text and metadata information.

The image segmentizer 110 may receive the image content 104 of thecontent item 102 from procurement 105. The image segmentizer 110segments the image content into a foreground image and a backgroundimage, with the foreground image corresponding to a segmented image 114.As mentioned, one or more embodiments provide that procurement 105receives or uses hints or a priori knowledge in segmenting an segmentedimage 114 from the background (e.g. such as through text, metadataand/or human provided knowledge). Thus, for example, the imagesegmentizer 110 may know to segment an image to separate a shoe, necktie, or other object from a background.

Moreover, one or more embodiments provide that the image segmentizer 110may segment foreground objects from other foreground objects. Forexample, image segmentizer 110, in the case where the image contains aman wearing a suit in a street, image segmentizer 110 may separate theneck tie and the suit jacket and the person wearing a suit separately,all from a background that shows the street.

The image segmentizer 110 may implement any one of manyforeground/background segmentation algorithms. One embodiment providesthat for a given image 104, a background is assumed to be at the sidesof the images, whereas the foreground can be assumed to be at thecenter. The intensity distribution of both foreground and background canbe obtained from the center and side pixels respectively. As an example,a mixture of Gaussian models can be learnt for the foreground andbackground pixels. Such models can be applied to the whole image andeach pixel can be classified as foreground and background. As anaddition or alternative, the foreground and background can be determinedby doing a statistical analysis of pixel distributions on the image,such as shown and described with a method of FIG. 2.

Under one embodiment, the segmented image 114 that is outputted from theimage segmentizer 110 is subjected to an alignment process 115. Thealignment process 115 may perform functions that normalize the alignmentof the segmented image 114. Normalizing the segmented image 114 mayfacilitate analysis of the image (e.g. recognition of its attributes)and/or the presentation of panels or other content containing thesegmented image 114 in some processed form.

According to an embodiment, the output of either of the imagesegmentizer 110 or the alignment process 115 may be provided to featureextraction 120.

Feature extraction 120 may detect or determine features of either thesegmented image 114, or a normalized segmented image 115. Data providedas a result of the feature extraction 120 (“feature data 134”) mayinclude data that descriptive of particular aspects or characteristicsof segmented image 114, as well as of the segmented image 114 as awhole. Certain features may be extracted that are class-specificdescriptors that apply to the image object as a whole, such as primarycolor, patters, shape, and texture. Features that apply to the segmentedimage 114 as a whole may be referred to as “global features”. One ormore embodiments also provide that feature extraction 120 determines“local features”, meaning those features that are localized to aspecific part or portion of the segmented image 114. Under oneimplementation, global feature may be extracted from the overall imageand segmentation mask. Local features may be extracted by dividing theoverall image into sub-regions, and then detecting some or all of thefeatures in each sub-region. Local features may also be extracted byidentifying and/or compiling characteristics of key points or regions onthe image.

Various embodiments described herein also provide for obtainingsub-regions in an image. In one embodiment, the image can be divideduniformly into sub-regions. In another embodiment, regions of key-pointscan be used to obtain the sub-regions. As an example, but without anylimitation, corner points, or homogeneous blobs can be used as keypoints in an image. In another embodiments, the regions can be selectedby random sampling on the image. In yet another embodiment, apreliminary segmentation algorithm can be executed on the image, andoutput resulting regions of segmentation can be used as sub-regions. Inaddition to determining/detecting class-specific global and localfeatures, one or more embodiments provide that the feature extractioncan, for some types of objects, determine features that are specific toa sub-class or even unique to a particular object. For example, featureextraction may identify features that uniquely identify a hand-wovencarpet, or even a face.

As described with another embodiment, global and local features are usedfor different types of visual search processes for an item. In theglobal case, a search process may seek to match an overall appearance ofan object to a specific feature that is provided in the search input. Inthe local case, a search process may seek a match of an object that hassimilar characteristics in a selected or specific area.

The detection and determination of features (global and local) may berepresented either quantifiably or by text. Analysis data generator 135may include components (vector generator 132 and text translator 136)for recording both kinds of data. Quantifiable data may take the form ofvectors, for example, which carry values that identify or aredescriptive of various features. The vector generator 132 may usedetected features represented by feature data 134 to generate a vectorrepresentation of a set of features for a particular object item. Thevector representation may comprise one or more signatures 128, which maybe generated for a particular segmented image 114. One segmented image114 may have multiple signatures 128, for one or more local featuresand/or global features, or alternatively one signature 128 that isrepresentative of the one or more local and/or global features. Thesignature(s) 128 of a particular image object may identify the givenobject (or a portion of an object) either uniquely or by a set ofclass-specific characteristics. A uniquely identified object may have aunique pattern, be handmade or even be a face of a person or animal.Additionally, signatures may be descriptive of both local and globalfeatures.

One or more embodiments include a text translator 136 that convertsclass-specific feature data 134 (or signatures 128) into text, so thatlocal and/or global features may be represented by text data 129. Forexample, an extracted global feature may be converted into text datathat is descriptive of the feature. In one embodiment, certain globalfeatures, such as a primary color, may be determined by value, thenconverted into text. For example, if the primary color is determined tobe a hue of orange, the text translator 135 may convert the featureextraction 134 into the text “orange” and assign the text value to afield that is designated for the type of feature. Processes andalgorithms for performing feature extraction and vectorization orquantification are described in greater detail below, including withFIG. 7-10.

Both signatures 128 and text data 129 may be provided to an indexer 160,which generates index data 162 for use with one or more indexes 164. Theindex data 162 may thus carry data that is the result of recognition orimage analysis processes. Subsequent searches may specify or use imagedata provided in the one or more indexes 164.

Under an embodiment, the object determinator 140 may include an imageanalysis component 146 for analyzing image data, as provided by any ofthe other modules, in order to determine information that may suffice asthe object identifier 143, in the absence of adequate text or metadata103. The image analysis component 146 may use image input, correspondingto the output of one or more of the image segmentizer 110, alignmentprocess 115, and/or feature extraction 120. The image analysis component146 may operate on either pixilated data or quantified data, such asdetermined by quantification/vectorization component 134. For example,under one usage or embodiment, text and metadata 103 may be analyzed bythe text analysis component 145 to determine object identifier 143 inthe form of a general category of the object carried in the content item102. This may be performed even before segmentation is initiated, oralternatively after some or all of the image processing steps areperformed, in order to generate a label or classification for objects inthe images of the content items. For example, some shape recognitionprocess may be performed on the result of the aligned image 115 toclassify the object as a “shoe”. Once classified, such an embodimentenables the feature extraction 120 to look for features that arespecific to shoes, such as heels, or straps. As an alternative oraddition, the shape recognition process may be executed on the imagedata output from the image segmentizer 110, so that the desiredalignment angle of the object may be determined from the image. Inanother embodiment, the image information can be used in conjunctionwith the text and metadata information. As an example, but without anylimitation, a classifier can be built, such as nearest neighborclassifier, support vector machines, neural networks or naïve bayesclassification. As a first step, a training set of items withcorresponding categories is obtained. Then, any of the aforementionedclassifier is built using any combination of metadata and image contentinformation. A new input item is classified using the learnt classifier.

With programmatic elements such as described with object determinator140, system 100 is capable of analyzing at least some content items 102on sources that carry very little information about what objects wereoriginally provided with the content item. For example, content item 102may correspond to a web page of a blog about an individual or a topicsuch as fashion, and the person maintaining the web page may lackdescriptive text on the image carried on the page. In such cases, objectdeterminator 140 may operate off an image output (segmented image 114,normalized image object 115, feature data 134) to programmaticallydetermine information about objects in the image. This may includeidentification of objects by type or classification, detection of thepresence of faces or persons, and even the possible identification ofpersons in the image. Additionally, presence detection may beimplemented for multiple features, where the detection of one featureserves as the marker for detection of another feature. For example, aperson may be detected from a marker that corresponds to a face, leadingto detection of the person's legs, and then his or her shoes.

For such images, one embodiment provides that the object determinator140 may use text and metadata to identify hints from surrounding textthat are not explicitly descriptive or readily associated with the imageof the content item. Such text and metadata, provided with or inassociation with the content item, may facilitate or confirm adetermination of the object identifier 143 by the image data analysis146. In one implementation where object identifier 143 is not readilydeterminable from text and metadata 103, the image data analysis 146 mayrun assumptions to determine if any assumption correlates to image data138 (with or without text and metadata 103). For example, in the casewhere the blog includes the name of a celebrity, the object image dataanalysis 146 may seek (i.e. run assumptions) foreground shapes thatcorrespond to clothing, such as shirts, pants and shoes, to see if anyprovide a general match. As an alternative of addition, the objectdeterminator 140 may seek faces or other readily detectable objects, andexclude or further use these objects to confirm identification ofobjects contained in the content item 102.

In one embodiment, the object image data analysis can be made fullyautomated to search for clothing and apparel items in an image. This mayinclude steps comprising some or all of (i) Face detection, (ii)Clothing segmentation (using the face location as a hint, and viaremoval of skin) (iii) Getting a confidence metric related to how wellthe segmentation is, (iv) Using a similarity matching based onhigh-confident clothing regions.

As a first step, the image is analyzed to find people in the image. Facedetection algorithms can be used as part of this step. Suitable facedetection algorithms for use with embodiments described herein aredescribed in U.S. patent application Ser. No. 11/246,742, entitledSYSTEM AND METHOD FOR ENABLING THE USE OF CAPTURED IMAGES THROUGHRECOGNITION, filed on Oct. 7, 2005. An output of the face detectionalgorithms include identification of the locations of faces in theimage. As a next step, the locations of the face and the eye are used asa prior to find the location of the clothing of the person.

It is generally known that the location of the upper-body-clothing iscertain distance below the face. Using this, one can start a regiongrowing based segmentation algorithm starting from a box roughly locatedaround the center of the upper torso. As an example, but without anylimitation, the location of the center of the upper torso can bedetermined based on the face location, and face size. The region growingsegmentation algorithm should also take skin color into account. Theskin color can be learnt via generic skin model obtained over a largeset of people dataset, or it can be learnt using the face location ofthe particular person. More specifically, the color distribution aroundthe face location is assumed to be the skin color distribution, and thepixels of such distribution would be excluded from the clothing regionsegmentation. Such a step would accomplish removal of a hand when aperson puts it in front of the clothing. In one embodiment, a regiongrowing segmentation algorithm can be used for segmenting the clothing.In another embodiment, a fixed size oval can be used to select theclothing location. In yet another embodiment, any embodiment ofsegmentation, such as matting or box-segmentation as described below canbe used. In this case, the location of face is used to automaticallygenerate a surrounding box to start the segmentation. In suchembodiments of the segmentation, skin color is optionally detected andrejected from the segmented clothing region.

Once the clothing is segmented, a confidence score is associated withthe clothing segmentation. This is necessary, mostly because some of thesegmentations is not very useful for further processing. Morespecifically, they can be too small, or they can belong to people thatare standing very close to each other so that the clothing of two peopleare segmented together. The confidence metric can be a function of manythings, including the size of the clothing region, how many faces thereare in the image, the smoothness and uniformity of color in thesegmented region, the gender of the person (which can be detected bymeans of a gender classifier), and existence and locations of otherregions and faces in the picture. Once a confidence is associated foreach clothing region, the high confident regions can be selected forfurther processing.

Once the regions are selected, a similarity search is applied towards anindex of 164, as part of a search 1260 as also described in thefollowing sections.

In various locations of system 100, results determined programmaticallymay be enhanced through manual enrichment. Accordingly, one or moreembodiments provide for the editor interface 160 to interface withresults of more than one modules or processes, for purpose of enabling amanual operator to manually confirm or reject results. As described withembodiments of FIG. 4A and FIG. 4B, the editor interface 160 may enablethe individual editor to confirm/reject results in groupings.

Classification and Determination of Object Identifier Information

FIG. 2 illustrates a category-mapping system for enabling humanoperators to facilitate a system such as described in FIG. 1. Anembodiment such as shown by FIG. 2 may use text and metadata ofindividual content items 102 to determine information about objectscontained in the images of a content item. The determined informationmay include, for example, object identifier information 143 (see FIG.1), which identifies an object of the content item 102 by one or moreclassifications. Thus, the text and metadata of content items 102 may beused to determine information about the nature of objects represented inthe images of the content items.

One or more embodiments use human operators 224 and knowledge 226 togenerate a reference list of words and phrases that identify or are usedtypically in association with content items of a particular classifier.For example, humans who are experts in fashion may tag or associatemerchandise items of fashion with new buzzwords and associations. Theseassociations may be maintained in an ongoing list that comprises theknowledge 226. The knowledge 226 may thus provide words or phrases fromwhich reference records 220 representing classifications may be defined.As described below, the reference record 220 may further include weightsand other factors for enabling programmatic classification based on thelist of words or phrases 222 contained in each record. The referencelist of words or phrases may be incorporated into reference records 220as a definition or partial definition of a corresponding category orclassification. In this way, each reference record 220 may define one ormore corresponding categories or classifications.

In the case of an e-commerce environment, for example, the descriptivewords used for merchandise items may vary based on editorial variationsof the different sources of the content items. This editorial variationmay depend in part on the nature of the source and/or its targetaudience. A site that targets merchandise for teenage girls, forexample, may refer to “sneaks” or “sneeks” for athletic footwear, whilesites for other audiences may use “athletic footwear for women”. Withsuch considerations, one or more embodiments provide that a givencategory (e.g. “woman's athletic footwear”) is associated with one ormore corresponding reference records 220. Each of the reference records220 define the corresponding category using words or phrases 222 thathuman operators 224 determine are descriptive of a correspondingcategory or classification 223. For example, “open toe”, “airy”,“strappy” and “leather bands” may all be mapped in one record to theclassifications of “woman's footwear” and “sandals”. The knowledgedatabase 226 may maintain lists and associations that are built by thehuman operators 224 over time, for purpose of recalling that certainphrases or words are indicative of a particular classification.

Operators 224 that create a category-specific reference record 220 mayleverage instances where specific words/phrases are relatively unique toone category, and that such words or phrases differentiate one domainfrom another domain. Operators 224 may also recognize that some wordsmay be common to multiple categories. The human operators 224 and/orknowledge 226 may also identify words/phrases that are common to thedemographic of the category (e.g. the demographic of people interestedin “woman's athletic footwear”). Examples of such classification includegender and age.

In one embodiment, human operators 224 assign weights 232 forcontributions of individual word/phrases, based on an understandingdeveloped by the human operator as to the vocabulary used by thedemographic that is associated with a particular category orclassification. The weights 232 may be provided before runtime as inputto the weighting influence 230. The weighting influence 230 may use theweights 232 to generate weighting parameters 235 for the text andmetadata of individual content items at time the content items 102 areassigned to a category 223 (see below). The weights 232 may reflect themeaning or importance of individual words, and as such, may be providedby human operators 224 who are familiar with trends in how vocabulary isused over time. The use of weights 232 recognizes that words or phrasesin one site have different significance or meanings than words orphrases at other sites.

In determining objected identification 143, content items containingimages, text and/or metadata may be programmatically analyzed andcategorized. The weighting influence 230 may receive or extractassociated metadata 238 (or text) from a source 201 of the content item102 and/or the content item itself. The weighting influence 230 mayassign the weights 232 to different fields of the metadata 238 that isretrieved from source 201, such as metadata that identifies a brand or anetwork site where the content item is provided. The assigned weights232 may then be processed for the metadata 238 to generate the weightingparameters 235, in a manner described below.

In an embodiment, the content items 102 are initially processed bytokenizer 240. The result of processing content items 102 includesgenerating tokens 242 from individual content items 102 that includewords and phrases contained in the text and/or metadata of the contentitem 102. The tokens 242 of individual content items 102 are forwardedto a categorizer 250. The categorizer 250 may use reference records 220and perform one or more processes to select which reference record 220provides the best definition or definitions of categories. In oneimplementation, the classifier 250 determines an overall matching scoreto one or more of the records. The score may be influenced by presenceof words that are unique to one category, versus words or phrases thatare associated with multiple categories. In addition, weightinginfluence 230 may include weighting parameters 235 that take intoaccount metadata 238 from the source 201 of the content item. Themetadata 238 includes identification (by human operators 224) of thegender or demographic of the network site where the content item islocated, as well as classifications made by the source of the contentitem (e.g. item provided in a particular labeled tab). The classifier250 is able to identify one or more classifications 252 (or candidatecategories) based on the combination of the tokens 242 as matched toreference records 220, and the influence of the weighting parameters 235provided from metadata 238 (and pre-defined by the human operators 224).The categorizer 250 makes determinations as to classifications 252 thatare to be assigned to each content item 102, or portions of individualcontent items 102. In another embodiment, the image content can be usedin addition to the metadata for object categorization purposes. In thiscase, the algorithm would also justify its result with the look in theimage, i.e. this image looks like a shoe or this image looks like awatch etc.

As an option, one or more embodiments include a manual editor interface260 that enables human operators to perform manual enrichment. Theinterface 260 may display classifications 252, along with image portionsof the content item, to human operators. In one embodiment, the displayof the content item images and the identified classifications are madein cascade, enabling the human operators to specify which of thedeterminations are not correct.

FIG. 3 illustrates implementation of the editor interface 260, under anembodiment of the invention. The editor interface 260, and resultingimplementation shown by FIG. 3, are illustrative of the manualenrichment component as a whole. The manual enrichment process isdescribed with an embodiment of FIG. 4. As shown by an embodiment ofFIG. 3, a page 310 of items 312 may be displayed to an operator. Thepage 310 may be assigned to a particular domain (category). As anexample, once the algorithm assigns all the “women's shoes”, images thatare assigned are shown all together in a page, and the editor confirmsthat each one of these choices is correct. In one embodiment, theconfirmation is in the form of rejecting incorrect determinations.

As an alternative or addition, one or more embodiments may utilizemachine learning techniques when applying classification determination(i.e. domain mapping). Under one embodiment, groundtruth data iscollected that has shopping items annotated with the domain thatcontains those shopping items. Machine Learning techniques like naïvebayes, logistic regression, and support vector machines can be used tolearn a classification model for domain mapping. The classificationmodel can be learned separately over each metadata field. Subsequently,a linear or non-linear combination of weights may be used to combinepredictions from the different fields. A single classification modelover all the metadata fields can also be learned.

A learning and prediction phase of the classifier 250 may be employed.The learning and prediction phase may use words that are weighted byparameters that are based on the inverse document frequency (IDF).Alternatively, words or phrases collected by human operators 224 may beused by the learning and prediction phase. Methods like naïve bayeslearn the prior probabilities of the domain and the conditionalprobabilities of the word given the domain. By Bayes rule andconditional independence assumption of the word sequence, the learnedprobabilities are used to predict the conditional probability of thedomain given the words in the metadata. The domain with the highestconditional probability is assigned as the target domain for the item.

Methods like support vector machines learn a maximum margin classifierin a sparse high dimensional space where the words in the metadata formthe dimensions of the space. In the prediction phase, the item'smetadata is converted to the high dimensional feature space. Theposition of this point in the high dimensional space with respect to thelearned classifier boundary determines the domain.

As illustrated by an embodiment of FIG. 3, an embodiment provides forperformance of manual enrichment by showing humans a cluster or groupingof items mapped to a particular domain. Human operators 224 may readily“eye-ball” the results and quickly remove incorrect mappings if any. Inone embodiment, a full page of items that are assigned to a particulardomain (category). As an example, once the algorithm assigns all the“women's shoes”, images that are assigned are shown all together in apage, and the user is asked to confirm that each one of these choices iscorrect. As such, embodiments recognize that it is much faster to askthe human a page of items as opposed asking for verification on eachitem one by one.

Manual Enrichment

The results of programmatic processes such as described with a system ofFIG. 1 may have a degree of error associated therewith. Recognition andanalysis of content items carries a certain inherent error. One or moreembodiments recognize the inherent error, and enhance results ofprogrammatic processes with manual enrichment.

FIG. 4A illustrates a manual enrichment process, according to anembodiment of the invention. A programmatic element 410 may obtainresults 422 corresponding to images 412 analyzed by any process orcomponent described herein, including with an embodiment of FIG. 1.These results 422 may be provided to an editor interface 420. The editorinterface 420 may display result groupings 422, which displays theresults 422 of the element 410 in a grouped presentation 430. A humaneditor 424 may provide feedback 421 that includes confirmation and/orrejection of individual results provided with the grouped presentation430. The editor interface 420 may convey the feedback 421 to rectifyand/or filter the results of the programmatic element 410.

As an example of how one or more embodiments may be implemented,individual results of the element 410 may be represented with panelsthat include an image and possibly a descriptive text element thatconveys the result to a human operator 424. In providing the resultgroupings 430, the panels may be displayed in cascade. For example, awebpage may include tens of panels, each carrying an image and/or textconveying the result of the programmatic element 410. An example of sucha result grouping is shown and described with FIG. 4B.

As an example, in an e-commerce application, the image may be of an itemthat is for sale, and the descriptive text element may provide theprogrammatic determined classification for the item. This may convey theresults of the object identifier 140 (FIG. 1), when content items 102are analyzed. For example, in the case where the content item is for ashoe that is on-sale, the text or metadata of the content item may beused to determine that the object in the image is a shoe. The term“shoe” or “boot” or “sneaker” may appear in the text or metadata.

As an alternative or addition, the element 410 may convey the resultsfrom the image segmentizer 110 (FIG. 1), which may be in the form of anoriginal image and a segmented image 114 (FIG. 1), with or without text.The human editor 424 may confirm the segmented image is whole, completeetc. Still further, the element 410 may correspond to alignment process115, where results 1422 convey object images 114 (FIG. 1) in alignedform. The feedback 1421 from human operator, when presented groupedpresentation 430, may be identification or selection of panels that showan object in misalignment. Likewise, the element 410 may correspond tofeature extraction 120, and the results may be derived from performanceof feature extraction 120. The results may be displayed individually,with the object image 114 on which extraction is performed. Numerousother processes may be presented for manual enrichment in a similarmanner.

As an alternative or addition to an embodiment, human editor 424 may beable to provide feedback that enables the identified erroneous result tobe rectified. For example, the human operator 224 may specify input thatcorrects the displayed alignment, via text or other kind of input.

FIG. 4B illustrates a manual enrichment process performed on results ofsegmentation process, such as performed by the image segmentizer ofsystem 100 (FIG. 1), under an embodiment of the invention. According toone implementation, a presentation 460 includes panels 462 comprisingresults of the segmentation process. Each panel 462 may be presentedwith a feature 470 for enabling the human editor to approve or rejectthe programmatic result (i.e. the segmentation on the image). In oneembodiment, a Boolean style feature is used for feature 470. The feature470 may, as default carry a “correct” value, from which the humanoperator may manually select “reject” when appropriate. Assuming theprogrammatic process is correct much more often than incorrect, thecascade presentation of panels enables one human operator to scanthrough thousands of panels in a relatively short time to reject thosepanels that convey incorrect results.

As mentioned above, a manual enrichment process such as shown anddescribed with FIG. 4B may be similarly applied to any otherprogrammatic process from which results may be generated.

Image Segmentation and Alignment

FIG. 5 illustrates a method in which image segmentation and alignmentmay be performed using a statistical analysis, according to one or moreembodiments described herein. A method such as described by anembodiment of FIG. 5 may be executed as part of the image segmentizer ofsystem 100, shown in FIG. 1. Accordingly, reference is made to anembodiment of FIG. 1 in describing an image segmentation algorithm ofFIG. 5.

In step 510, a programmatic statistical analysis of a pixel distributionis applied to an image to determine foreground and background elementsof the image. Let I be an image. Let F be the features of the image foreach pixel. Examples of features are grayscale intensities, values inRGB color space, CIE-L*a*b* or any other color space, or texture andother image features that are derived from the raw image data. Let P bea set of pixels that are at a distance of k pixels from the imageboundary. Let m be the median vector of the feature vectors for thepixels belonging to set P. Under one implementation, the followingalgorithm can be used for segmentation:

1) For each pixel (x,y), a calculation is made of the feature distancefrom m: d(x,y)=∥F(x,y)−m∥^2.

2) Label all pixels for which d(x,y)<T as background, and label allpixels for which d(x,y)>=T as foreground. T is a predefined threshold.

3) A connected component analysis is performed for the backgroundpixels. Connected components are identified when they are of size lessthan M and label pixels belonging to these connected components asforeground. M is assumed to be a predefined threshold.

5) A connected component analysis is performed for the foregroundpixels. Connected components are identified that are of size less thanM. Pixels belonging to these connected components are labeled asbackground.

5) As an additional option, a connected component analysis is performedon the background to identify connected components that do not touch theimage boundary. The pixels belonging to these connected components arelabeled as foreground.

6) If the foreground or background region size is less than n percentageof the full image size, the segmentation algorithms and processes areperformed again, with different parameters, until this condition isinvalid. The value of n is a predefined threshold.

The final labeling returns a map in which each pixel is labeled eitheras foreground and background. The foreground pixels define the segmentedimage 144, and can then be used for alignment and feature extraction, asperformed by other modules of system 100.

As an addition or alternative, other segmentation algorithms may beperformed. Among such algorithms are Min-cut Max-flow basedsegmentation, Random walk and first arrival probabilities based imagesegmentation, Curve evolution and active contours.

In one embodiment, a human is asked to draw a box around theobject/product that tightly encases the object. The parts of theoriginal image that are outside the box are considered to be background(we will refer to these as background seeds). A subset of the imageinside the box is assigned to be foreground (similarly called foregroundseeds). The subset is chosen depending on object-specific knowledge, forexample, for t-shirts the middle of the box is a good foreground guess,while for pants the middle top is a good stable guess. The knowledge ofwhat regions are typically good foreground guesses can be extracted byextracting statistics from a training set of segmented object/productexamples. The complete segmentation is then obtained by enforcing theassumption that colors and textures are coherent in the foreground andbackground regions, respectively.

In one implementation, this coherence is enforced using a Markov RandomField over the image pixels, which associates edges with high similarityscores to pairs of adjacent image pixels with similar color and texture,and edges with low similarity scores to pairs of adjacent image pixelslying on boundaries between homogeneous regions. Using this pairwisepixel similarity graph, we can solve for a partitioning of the pixels,that is consistent with the foreground/background seeds and cuts the setof edges with minimal similarity cost. There are different similarityscores possible, based on various models of local region color andtexture. In another implementation, the Markov Random Field is augmentedwith additional color models for the dominant colors in the foregroundand background, respectively, which factor as a separate term. In oneform of this embodiment, the input of foreground and background can beobtained via a person scribbling on the foreground and backgroundrespectively.

Irrespective of the type or implementation of the segmentationalgorithms, one or more embodiments provide for the use of manualconfirmation to confirm segmentation. Embodiments recognize thatprogrammatic or machine-based segmentation may have error that makesresults less reliable than what can be provided by a person.Accordingly, step 520 provides for manual confirmation of thesegmentation performed on the images 104, in determining the segmentedimage 115. In one embodiment, manual confirmation is performed bydisplaying to a human editor the segmented image 115, and enabling thehuman editor to accept, reject or edit the segmented image 115determination.

Once images are segmented, optional steps of alignment and preprocessingmay include an objective to align or format images into to a canonicalview, and to determine and focus on various parts of the object in thecontent item. In one embodiment, focus items are category-dependent.Accordingly, step 530 provides that an object classification isdetermined or retrieved. In one embodiment, information provided throughobject determinator 150 is used to determine the object classification.Different levels of specificity may define the object class, dependingon, for example, the information available from the metadata, textand/or source of the content item 102.

Step 540 then determines the focus item(s) of the image, based on thedetermined classification. For example, the image of a content item maybe known (or determined) to carry a watch, in which case the focus itemmay correspond to the dial area. In the case of a woman's shoe, thefocus item may include the regions of the toe and the heel.

In step 550, one or more rules or algorithms that are specific to theclass of the object carried in the image is implemented to perform adesired alignment. Different types of objects include different shapesand features which need to be accommodated in the alignment process.

FIG. 6A-6C illustrates results of an embodiment as applied to an imageof a shoe. In FIG. 6A, the content item image 104 (FIG. 1) correspondsto a shoe image. The image may be retrieved from, for example, a pictureon a web page, or a record for display on a web page (such as on ane-commerce site). FIG. 6B illustrates the result of segmentationperformed on the image 104 (FIG. 1), corresponding to the segmentedimage 114. For the case of shoes, in order to perform alignment, thepixels of the vertical foreground are summed. In other words, for eachcolumn of the image, an embodiment sums all the pixels that areforeground in that particular column. The example of the sum is shown inFIG. 6C. Then the sum graph is analyzed, and the side that has thehigher slope is selected as the heel.

For the case of jewelry items, images (particularly e-commerce images)are often captured in numerous orientations. Without alignment,comparisons performed in visual searches can be more difficult. It isthus desirable to first transform the images to a canonical view, inwhich the items have the same orientation. This task has varying levelof difficulty, depending on the geometry of the particular item. Onerobust technique of rotating objects relies on the ability to compute asegmentation mask of the object. Such a mask is shown in FIG. 6B, wherethe white pixels encode parts of the object, and black pixels encode theimage background. In previous sections, an embodiment includes differentways of computing the segmentation mask of an object. Given this mask,in one embodiment, Principal Component Analysis is performed over thelocations of the white pixels. This is intuitively equivalent to fittingan ellipsoid to the object shape. The direction of the largesteigenvector corresponds to the principal direction along which the massof the object is spread. However, if the ratio of the first to thesecond largest eigenvalue is small, the principal direction is notprominent, and a determination may be made to leave the image in theoriginal orientation. Typically watches, rings, bracelets and necklacesare items for which such an alignment process works well, provided goodsegmentation is available. For rings and necklaces, the focus may be ofa large stone or pendant, and ignoring it in the computation of the PCAcan further improve the quality of the alignment.

In another embodiment, Hough transform can be used to find the roundparts of an image. For Hough transform, each pixel of the image is takenafter the segmentation mask, and the circle or ellipse that passesthrough each 4-point selection is recorded. For each selection of4-points, least square fitting can be applied to find the best matchingcircle or ellipse. In order to speed up the process, this process mightbe applied on a large number of random selections, as opposed to verypossible selection of four points.

In another embodiment, the edges of the segmentation mask is foundfirst, which serve as the boundary pixels. Then the circles or ellipseare fitted to the boundary pixels only.

Once all the circles and ellipses are found, a voting is applied on allthe circles to find the circle that has the biggest number of hits. Thevoting can be applied on one aspect first, such as the center locationof the circle and then the radius. The final best vote(s) are declaredas the winner circle/ellipse(s). In the case of watches, this algorithmcan be used to find the face of the watch. Once the face is extracted,more attention can be applied on the face area either while applyingfeature extraction, or while doing feature (similarity) matching.

Once an automatic segmentation algorithm is applied on the images, theresults can be reviewed by the human beings for quality assurance. Inone embodiment, a page that includes many images and their correspondingsegmentations are shown to the humans. The person would click yes or noif the segmentation is correct or wrong respectively. An example of asuch user-interface system or front-end is provided in FIG. 13. Thequery items that are clicked “No” can then be queued up for furtherdetailed processing. The detailed processing might involve painting allthe pixels of the foreground by a simple user-interface and virtualpaintbrush, or drawing a rectangle around the object.

Feature Extraction

A feature corresponds to a visual characteristic of an image of anobject. A feature is extracted when it is identified, or substantiallyidentified, and then represented as data that is separate from theimage. In one embodiment, extracted features are quantified in the formof vectors or values. In another embodiment, some features areidentified and then represented as text data.

One or more embodiments provide that images of objects are analyzed inorder to identify features of those objects. These features maycorrespond to colors, patterns, shapes, or other physicalcharacteristics. Extracted features may then be used in the applicationof the analyzed images. Applications for extracted features include, forexample, enabling programmatically generated descriptive terms, orenabling search inputs that are specific to identified features.

FIG. 7 illustrates a feature extraction module, under an embodiment ofthe invention. A feature extraction module 710 may operate independentlyof any other module or component shown. Any image input 712 that conveysan object may be used. Techniques such as described below may extractfeatures 714 from the image input 712. In one embodiment, the imageinput 712 includes a segmented image, that is aligned, through use ofelements such as described with an embodiment of FIG. 1. As such,embodiments such as described with FIG. 7-9 may be used to implement thefeature extraction module 130 of the system shown in FIG. 1.

FIG. 8 illustrates modules of a feature extraction system or module(such as shown in FIG. 1 or FIG. 7), under an embodiment of theinvention. A feature extraction system 800 may include processes fordetermining both global features 812 and local features 814. Asmentioned, the global feature 812 may correspond to a physicalcharacteristic or attribute that applies to the object of the image as awhole. A global feature extraction module 820 may be used to extract oneor more global features 812 from a given image input. In order todetermine local features 814, the image input (e.g. image of an object)may be subjected an image region identification process 830, whichidentifies regions 832 of the object in the image. A local featureextraction module 840 may execute to determine the local features 814using the identified regions 832.

FIG. 9 illustrates components that comprise the global feature module,according to an embodiment of the invention. According to one or moreembodiments, the global feature extraction component 820 includes acolor feature extraction component 910, shape feature component 920,and/or text feature component 930. The color feature extractioncomponent 910 may be used to determine different types of global colorfeatures. One global color feature is the dominant or primary color(s)of the image object.

A process 912 of color feature extraction component 910 may performanyone of numerous techniques for the determination of a primary colorof the object in an image. In one embodiment, a dominant colordescriptor is used. This color feature extracts the dominant colors ofan object. The input is a segmented image of an object and the output isa list of the dominant colors and their weights.

In one embodiment, process 912 determines principle colors using ak-means clustering algorithm. A red-green-blue (RGB) space or any othercolor space (HSV, lab etc) can be used. A k-means clustering algorithmmay be implemented by first sampling a fixed number of RGB pixels in agiven object image (e.g. object image 114 uses in system 100 of FIG. 1).A set of k centers (not too close together) are chosen from the pixels.The k-means is used to determine the final cluster centers (in RGBspace). If two clusters are close together, then they are merged. If acluster is two small, it is removed. The final cluster centers are thedominant colors and the cluster size is normalized to be the weight.

A process 914 of the color feature extraction component 910 determines afeature distance between two dominant colors in an image. Given two ormore color features on one object, or multiple objects or physicallydistinct features in one object, a distance between the two features maybe computed by matching the dominant color of the first feature to theclosest dominant color in the second feature. The computation of thedistance may utilize a pairwise distance function described below, whileat the same time accommodating the determined weights, so that all thecolor weight from the first feature is matched to all the color weightof the second feature. The final distance is the weighted sum of all thepairwise color distances. The pairwise distance is the color distancebetween two individual colors. This distance is composed of threecomponents. The first component is the L2 distance between the two RGBvectors. The second component is the angle between the two RGB vectors.This angle is sensitive to color changes, but insensitive to changes inlighting. The third component is a normalization term based on thelengths of the two RGB vectors. This normalization term prevents brightcolors (with high RGB vectors) from being penalized unfairly. Inaddition, colors with RGB vectors below a certain length (very darkpixels) only use the L2 distance of the RGB vectors.

As an alternative or variation to how processes 912 and 914 areperformed, colors (as global features) may be determined using colorhistograms of an overall image of an object.

As another alternative or variation to how processes 912 and 914 areperformed, an image may be divided into multiple regions, and anindividual color histogram may be obtained for each regions. Such anapproach not only captures the color variations, but may also capturethe layout distribution of the colors.

Still further, another embodiment provides that a perceptual color spaceis developed. A collection of color names are obtained (e.g. blue,green, pink, red, black). Then, colors covering the RGB space are shownto humans, who provide feedback that matches colors to the correspondingnames. In this way, a variety of color tones that are assigned to aparticular color name are identified. A machine learning technique maybe executed to learn the mappings between the colors vectors and thecolor names. As an option, a color look-up table can be used for thispurpose.

A shape feature component 920 may identify a global shape feature froman image object. The shape feature component 920 may use thesegmentation mask for a given image object in obtaining the shapefeature. In particular, the component 920 determines the shape featuresfrom the boundaries of the foreground pixels. In one embodiment, acontour feature can be used. First edge detection is applied to detectthe boundary pixels. Next, one of the several techniques can be appliedto the collection of boundary pixels. In one embodiment, the boundarypixels are put into a vector. Then, Elliptic Fourier Descriptors, orMoment Descriptors can be calculated on the collection of the boundarypixels.

In another embodiment, the shape feature component applies a PrincipalComponent Analysis on the collection of boundary pixels. First, the listof boundary points are aligned. For example, a north-most point in theimage can be chosen as the starting point of the boundary sequence. Thenthe boundary pixel locations may be written to a vector in order. ThenPrincipal Component Analysis is applied on this list, and the first fewk Eigen Values are chosen as the representative of the contour of theimage.

In another embodiment, the component 920 uses the shape features thatrepresent the areal distribution of pixels. One such example isRegion-Shape-Descriptor (RSD). The region-based shape descriptorutilizes a set of ART (Angular Radial Transform) coefficients. ART is a2-D complex transform defined on a unit disk in polar coordinates. RSDtakes into account all pixels constituting the shape, including both theboundary and interior pixels. It is therefore applicable to objectsconsisting of a single connected region or multiple regions, possiblywith holes.

In another embodiment, the shape feature component 920 is configured tocapture the layout of the image that can be used. Edge histogramdistributions (EHD) is one such feature. The edge histogram descriptorrepresents local-edge distribution in the image. Specifically, dividingthe image space into n-x-n sub-images, the local-edge distribution foreach sub-image can be represented by a histogram. To generate thehistogram, edges in the sub-images are categorized into five types:vertical, horizontal, diagonal (45 degrees), diagonal (135 degrees), andnon-directional edges. Then the combination of these histograms on theoverall sub-images serve as a layout descriptor for the image. Thisfeature can also be used as a texture descriptor. As such, it wouldcapture both shape and texture features of the image.

In order to make image matching robust against variations such astranslation, the implementation of EHD also derives semi-global andglobal edge distributions by averaging the original local edgehistograms on larger blocks and on the whole image. In addition, one ormore embodiments disclose a further improvement to standard EHD to makeit invariant to object flips (for examples, matching a shoe pointing toleft with a shoe pointing to right). In that case, EHD features areextracted twice, one from the original image, and one from the flippedimage. The combination of the two features are complimentary in thatthey ensure that flipped images can match against other.

The texture feature component 930 identifies texture features from theimage object. More specifically, the texture feature component 930determines the patterns inside objects. These may include, for example,checkered or stripped patters on clothing or luggage. In one embodiment,texture feature component 930 includes several convolution filters,which are passed through the object image. Each of these filters capturedifferent characteristics about the image, for instance differentfilters might capture horizontal edges, vertical edges, tilted edges,no-edges, and/or different resolution edges. Texture descriptors can becreated by getting the distributions of these features. For instance,the histograms of vertical and horizontal edges can be used to capturethe difference between the horizontally striped shirt versus avertically striped shirt, versus a checker pattern shirt. Thedistributions can be obtained in various ways. In one embodiment,texture feature component 930 obtains the histograms of the scalarconvolution outputs. In another embodiment, texture feature component930 combines the outputs of multiple convolution filters into amulti-dimensional histogram. As an example, vector quantization can beused for this objective. First, k-mean clustering can be applied on acollection of convolution outputs. This step determines the clustercenters. Then vector quantization can be applied for each image and adistribution of the convolution outputs is obtained per image. Thisdistribution is used as a feature vector for representing the texture ofthe input image. In one embodiment, texture feature component 930obtains texture features using only data from within the segmentationmask.

In another embodiment, the texture feature can be obtained from a box orother geometric shape that is completely included in the segmentationmask. In another embodiment, Gabor filters can be used to extract thetexture features. This feature is a Gaussian weighted sinusoid, and isused to model individual channels in an image, where each channelcorresponds to a specific type of texture. Then for each channel, sometype of statistics such as the energy, and energy deviation or energydistribution are saved.

The metadata feature component 940 determines metadata features fromcontent associated with the object image. In an embodiment of FIG. 9,the term metadata is used to denote the text fields associated with aprocured (e.g, through web crawling) content item. These include theitem title, description, brand, associated keywords, price, uniqueproduct number and product categories the item is thought to belong to.In an embodiment, the metadata feature component 940 may be implementedas part of the object identifier 140 of FIG. 1, including as part of thetext and metadata analyzer 145.

Such metadata has several different uses. One major use is to extractfeatures that would then be used to identify the similarity of differentobjects. Important clues for similarity of two product items can beextracted from their metadata descriptions. For example, metadatausually contains the item type (casual sandal), brand (TeVa), adescription of the item containing useful expressions such as rubbersole, strappy, leather. Based on these features, a similarity scorebetween two items can be obtained.

In one embodiment, the relative importance of the different terms (wordsand phrases) in the metadata is estimated using the inverse documentfrequency (IDF). The IDF score for a term is the logarithm of the numberof all product items in a sample collection, divided by the number ofproduct items in that collection whose descriptions contain the term.Intuitively, when a term is relatively rare in the whole collection ofdocuments, it is considered more important for similarity. A descriptionof an item can thus be represented using a sparse vector containing theIDF scores for the terms that appear in its description. The similarityof two items then can be defined as the cosine of their IDF scorevectors.

In another embodiment, the text in the different text fields (e.g.title, associated keywords, description, brand, etc.) may contain termsof different level of importance with regard to the perceived similarityof two items. Hence the IDF term vectors for each of those fields can becompared separately, and combined to obtain the final score. Thecombination can be a simple linear function of the separate text fieldscores, or a more complicated non-linear combination. This combinationcan be defined by human operators, or learned on a collected data set(ground truth dataset) of similarity data, where users specify whichitems they think are similar, and which are different. Any standardclassification or regression method, including but not limited tologistic regression, least-squares regression, linear discriminantanalysis, boosting, support vector machines etc. can be used in learningthis combination.

In another embodiment, human operators can be employed in identifyingbuzzword expressions. These expressions are words or phrases deemed tobe of particular importance for determining item similarity (e.g.pointed-toe shoes, lace-up shoes, analog watch). The IDF termsassociated with these buzzwords can be weighted separately from therest; the tradeoff can be learned on a collected set of similarity dataand standard classification or regression algorithms.

In another embodiment, human operators can specify additionalinformation about the buzzwords. There are different ways to describethe same concept, hence may be beneficial to identify synonym phrasesthat all correspond to a particular buzzword. Also, buzzword expressionscan be combined into sets. For example, a set of buzzwords thatdescribes different kinds of shoe toe styles would include buzzwordssuch as split toe, round toe, square toe, open toe, or pointed toe. Asthere is a fairly limited number of such sets, the IDF scores for theseitems can be ignored, and instead the importance of each buzzword setrelative to the rest can also be learned using a collected set ofsimilarity data and standard classification or regression algorithms.

Another embodiment addresses the issue that text fields do not alwayscontain all the appropriate buzzwords for the item. The set of buzzwordsdescribing the item can be enriched based on visual cues. For example,the item colors can be estimated from the associated picture of the itemusing a clustering algorithm, such as k-means or Gaussian mixturemodels. The top colors and their amounts that are identified can beadded to the item metadata. Similarly, separate detectors can be builtthat detect certain properties of product items (e.g. which shoes havestilettos) in the images of the items. These detectors can be trained ona labeled collection of items, using standard image classificationalgorithms such as but not limited to boosting and support vectormachines. Buzzwords detected above a given level of certainty are addedto the metadata.

In another embodiment, human operators can do manual enrichment on theset of buzzwords. The simplest embodiment has humans scanning all itemsof interest to identify a particular buzzword (e.g. whether shoes havestilettos). A more advanced embodiment pre-filters the candidate itemsuses automatic enrichment methods such as those described in theprevious paragraph. In addition to image cues, textual cues can also beused in manual enrichment. For example, if the item is labeled assandals, it can be determined with reasonable certainty that a number ofbuzzwords such as boots, knee-high, closed toe, etc. do not apply withvery high probability.

In another embodiment, the image classification techniques can be usedto add metadata features. For instance, a “Strappy” shoe detector can beused to identify that a shoe is strappy, and the word “Strappy” can beadded to the metadata of the particular item.

FIG. 10 illustrates a method for determining and extracting localfeatures from an image object, under an embodiment of the invention. Amethod such as described may be performed by any of the featureextraction modules described above, including with an embodiment of FIG.1 or FIG. 7. A method such as described may be implemented on an objectimage.

As mentioned, the feature extraction module such as described with anembodiment of FIG. 7-9 may be configured to extract local features.Among other benefits, the use of local features provides the user withthe ability to fine tune or limit a visual or image search by specifyingsub-regions of a given region for use as the basis of a search query.The features from a sub-region of the image enables a search based ononly a sub-region of the image. Thus, for example, a user may perform avisual search for shoe, while specifying a heel shape or an image of aheel as the query. In describing a method of FIG. 10, reference may bemade to elements or components of other figures, for purpose ofillustrating suitable elements or components for performing a step orsub-step being described.

In one embodiment, local HOG features can be obtained in key points ofan image. The steps of this algorithm are described next.

In order to facilitate the use of local features in an image, step 1010provides that each image is normalized such that all images have samebounds and extents. Normalizing the image includes normalizing anoverall size of the image. With reference to an embodiment of FIG. 8,step 1010 may be performed by, for example, image region identificationprocess 830, as part of a process of identifying regions of an objectimage.

Additionally, step 1010 is more relevant for searching online shoppingimages where there is often only one object in the image under analysis.In such cases, the act of pre-normalizing the image to a fixed sizeavoid issues that may otherwise be caused by the scale changes. Forimages present in online shopping websites, the system estimates a tightbounding box around the segmented foreground in the image and normalizesthe image to a pre-determined fixed resolution.

Step 1020 provides that key points or regions of an object image aredetermined for the object image. Key points or regions are defined asthose characteristics image regions which have high auto-correlation.The main characteristics of such key points or regions include (i) a keypoint or region is reliably detectable even if an angle of image capturevaries, and (ii) the point or region detection performance is repeatablefor a given key point or region detector provided that locally the imageregions are visually similar. Examples of such key point detectorsinclude Harris or Hassian detectors, and scale, rotation invariantHarris-Laplace detector. With reference to an embodiment of FIG. 8, step1020 may be performed by, for example, image region identificationprocess 830, as part of a process of identifying regions of an objectimage.

In step 1030, descriptors are computed around these key points orregions. These descriptors attempt to capture the shape, color ortexture around the key points or regions in a high dimensional featurevector. For example, histograms for color channels can be used as colordescriptors, three-dimensional spatial-orientation histograms of imagegradients as shape descriptors, and Gabor filters as texturedescriptors. As another example, one can use Color-Spatial Histograms(CSH) as color descriptors, Histogram of Oriented Gradients (HOG) orGeometric Blur feature as shape descriptors, and Gabor filters astexture descriptors. Most of these descriptors require spatial contextaround the key point or region. If a key region detector is used, thenthe spatial context is automatically defined to be proportional to theregion of the region detector. If a key point detector which does notprovide any scale information is used, then the system uses a userconfigurable predefined M×M pixel region around the key point as thespatial context. The following includes description of embodiments inwhich Color Spatial Histogram features (CSH), and histogram of orientedgradients (HOGs) are implemented.

Color Spatial Histogram features (CSH): These features can be used as analternative to simple color histograms. To compute these descriptors,the local image region around the key point or region is divided intopre-configured K×K cells. Each cells defines local regions of L×Lpixels. A color histogram is computed for each of these cells. To avoidaliasing effect, pixels which are close to cell boundaries are votedusing bilinear interpolation. Any of RGB, Lab or Luv color space can beused to compute color histograms. The color histograms are thennormalized by performing L2 or L1 normalization. The advantage of thesecolor-spatial histograms is that they also capture the spatial locationof the colors with respect to key point and/or region and hence havemore spatial-discriminative powers.

Histogram of Oriented Gradients (HOGs): These features are based onimage gradients. According to one embodiment, processing chain of HOGcomputation involves following steps: (i) Computing an image gradient inthe local spatial region. For color images, the gradient is computed foreach color channel and one with maximum magnitude is taken as finalgradient value. Optionally, one can compress the image intensities bycomputing square root of image intensities, which helps dampen theeffect of sudden gradient changes in the image region. (ii) Next, localimage region may be divided around the key point and/or region into K×Kcells with each cell of L×L pixels. (iii) A three-dimensionalspatial-orientation histogram is computed where the weights in thehistogram are weighted by their gradient magnitude.

Step 1040 provides that a similarity measurement amongst key points orregions is enabled. This enables the identified local descriptors forform the basis of data that is searched against, for example, anotherlocal descriptor serving as a query. Similarity and feature searching isdescribed with additional detail and variations in following sections.According to an embodiment, histograms from all cells are concatenatedinto one single feature vector. This vector is normalized by L2 or L1normalization to provide invariance to color changes, and the finalresult is used as the local shape features. Once the features areobtained, for each descriptor a similarity measure is defined. Thesimilarity measures allow the system to measure the similarity of onepoint with respect to any another point. The system uses one of thefollowing distance measures: L2 metric (i.e. Euclidean distance), L1metric, L-∞ metric, or Bhattacharya coefficient. Other measures whichcan be considered are the KL or Jensen-Shannon divergence.

As an alternative or addition, a Local Similarity Search Algorithm maybe used. A system implementing such an algorithm may be initialized byloading all key points and/or regions and their correspondingdescriptors for each image in the database. If the number of images isso large that all descriptors can not fit into the RAM of one machine,then a set of machines is employed and the images and corresponding keypoints and/or region descriptors are distributed on these machines. Thekey requirement in the search algorithm described below is to computenearest neighbors for a given point. However as the number of key pointsor regions easily becomes very large, a naive linear search is sometimesnot possible. In such systems, a component or system may computespill-trees or Locality Sensitive Hashing on the descriptors. Thesealgorithms provides the capability of fast returning the top N nearestneighbors for any given point.

With extraction of local features, subsequent image searching of localfeatures may be performed on a collection of images. In one embodiment,when a user selects an image region, the system searches for all keypoints or regions underlying the user selected region. These key pointsand/or regions form the query points or regions. If user selects a newquery image or does not make a selection then all points and/or regionspresent on the image are taken as query points and/or regions.

For each query point and/or region, the system searches for top N mostsimilar key points and/or regions. The similarity is defined by one ofthe above described similarity metrics. Each of these N nearestneighbors are mapped to the corresponding image in the database. Thesemapped images form the potential search results. A default rank of 0 isassigned to each of these images. Sometimes multiple points or regionsfrom an image in the database matches to a query point or region. Thisresults in many to many mappings of the points.

Depending upon the user configurations, one of the following algorithmsmay be used to assign ranking to images. For example, one of thefollowing algorithm may be programmatically called once for eachpotential image which is to be ranked.

Simple Voting: This algorithm first performs a greedy best one to onematching (in terms of values returned by the similarity metric above)for each query point and/or region. This converts the many to manymappings above to one to one mapping. The algorithm then convertsdistances returned by similarity metrics to goodness scores. As anexample, for L2 metric, the system employs exponential of the L2distance as the goodness measure. The search algorithm keeps count ofnumber of votes (obtained by summing goodness scores for each querypoints and/or regions) for each image. After a vote is computed for eachpotential image, the output of the search algorithm is the images sortedin descending order of these votes.

As an alternative or variation, another technique after one to onemapping is to treat these feature distances as likelihoods of matchings,and sum the distances for all points in the local region to compute theoverall dissimilarity measure. An optional step is to compute occurrencefrequency of each feature. The goodness scores during matching is thendown-weighted such that more frequently occurring features have lessweight. This ensures that only most striking features are used insimilarity matching.

Translation Constraint: This method tries to find the best twodimensional translation that maps the query image points or regions toeach of the images in the potential image list. The system employs theRANSAC algorithm to robustly compute the matching. The images are sortedin descending order of the number of points and/or regions, whichsatisfy the translation constraint.

Similarity Constraint: This algorithm is similar to the translationconstraint but here the algorithm tries to find the best 2-D similaritytransformation that maps the query image region to each of the image inthe potential image list.

Affine Constraint. This algorithm is similar to the similarityconstraint but here the algorithm tries to find the best two-dimensionalaffine transformation that maps the query image region to the each ofthe image in the potential image list.

With further reference to embodiments of FIG. 7 thru FIG. 10, and as analternative or addition, one or more embodiments enable the user to fusesearch results by defining multiple selections. Here the user selects animage region and the system returns the most similarly ranked images.Now the user can select another image region and ask the system toreturn intersection or union of the two search results. More than twoselections can be defined in a similar fashion. This allows user tofurther fine tune their results by refining their queries in a stepwisemanner.

As still another additional or alternative to embodiments of FIG. 7 thru10, one or more embodiments enable features to be extracted that areidentified as global features. However, such features are extracted onlyin a local region (tile). This allows users to search objects based onlocal appearances. The input is the segmented image of an object. Thebounding box of the segmented object is tiled into a fixed number oftiles (M×M) and (global) image features (such as dominant color and EHD)are computed for each tile. The feature is stored as the concatenationof all the features of all the tiles. In one embodiment, a local searchis performed by selecting a rectangle in the query image. The tiles thatintersect the rectangle are used in the image search. The featuredistance is the sum of the distances of all the features in thecorresponding tiles, weighted by the area of overlap between the tileand the query rectangle. The final distance is normalized by the numberof tiles.

In another embodiment, the tile based features and the HOG based(key-point) based features can be combined to increase the accuracy of alocal search. In yet another embodiment, these algorithms can run in acascade, for instance the tile based algorithm can run first todetermine the candidates, and the key point based algorithm can beexecuted next.

According to an embodiment, the local feature extraction component 830(FIG. 8) may be configured to automatically detect regions and extractfeatures on sub-regions. In one embodiment, a Content-based ImageRetrieval (CBIR) algorithm or process may be implemented. CBIR refers toprocesses that attempts to find images similar to a given query imageusing visual information instead of contextual text meta-data. Mostconventional methods base the search on global (whole-image-wide) visualfeatures such as color, texture, and shape. Although such methods can beeffective in locating globally similar matches, they may miss importantregional features. In order to capture regional features, automaticregion segmentation (perhaps plus manual verification) is usuallyemployed to obtain the meaningful regions before feature extraction canbe carried out on those regions. However, region segmentation is stillan open problem and any region segmentation method will have deadcorners. Although human verification and editing can make up for thecomputer errors, such manual effort can be slow and add significantcosts in high-volume real-world applications. One or more embodimentsdisclose a cost-effective method for region-base image search that canbe used under one embodiment.

System for Enabling Search of Analyzed Images

One primary use for an image analysis system such as described withembodiments provided above is to enable search of images and othercontent for applications such as e-commerce. As mentioned elsewhere, oneuse for a system or method for analyzing content items that carry images(such as described with an embodiment of FIG. 1) is to enable a searchsystem that enables queries to specify image data or values. Aconventional image searching system is normally based on text queries.For example, popular search engines on the Internet use text meta-dataassociated with each image to determine the keywords and search imagesbased on these keywords. This conventional approach has severaldisadvantages. Among the disadvantages, (i) the algorithms are based onthe text surrounding an image not on the content present in the image,and (ii) the user or source of the search query is unable to fine tunethe search by specifying sub-regions on the query image. As such, thereis a need for enabling image searching using queries that specifycontent in the form of image data, or otherwise non-textually.

CBIR algorithms search for images similar to a given query image usingvisual information. Most algorithms are based on global (whole imagewide) visual features such as color, texture and/or shape. Such methodsare effective in locating globally similar matches, but may missimportant regional features. Moreover they do not provide enoughflexibility to the user to fine-tune their searches by selecting regionsof specific image.

In contrast, embodiments described herein provide effective methods forsearching visually similar images from a large database (e.g. of orderof million of images) all the while providing user the flexibility offine tuning their searches by giving relevance feedback. One or moreembodiments also include human assistance and quality assurance in manyaspects of processing, such as categorization, a segmentation,extraction of key points, all of which can be employed as preprocessingsteps in the system.

A system such as described may be used in connection with otherembodiments described herein to enable search of records carryingimages, using anyone or more of different kinds of records.

FIG. 11 illustrates a search system for enabling search of images,according to an embodiment of the invention. In one embodiment, a searchsystem of FIG. 11 may be combined with a system of FIG. 1, and/or withcomponents described therein or elsewhere in this application, to enablesearching that are based at least in part on queries that specify imagedata and/or values. In such embodiments, embodiments described with FIG.1 and elsewhere in this application may be used in order to aggregatedata corresponding to searchable image data.

In FIG. 11, a search system 1100 includes modules in the form of auser-interface 1110, search 1120, and procurement 1130. The modules mayoperate in connection with a content analysis system 1140 that analyzesthe image, text and metadata content of records. Additionally, thesystem 1100 may include an index 1150 which stores index data 1152generated from the analysis of images and content item. The index data1152 may be generated as a result of the performance of the contentanalysis system 1140. System 1100 may include a record data store 1160that holds records 1162 that include content items analyzed by thecontent analysis system 1140. Procurement 1130 may retrieve and populaterecords 1162 with record data 1164.

According to an embodiment, the procure 1130 retrieves recordinformation about the content item 1102 that can be used to categorizeor search by classification. For example, in an e-commerce application,the record data 1164 may correspond to the price of the item for sale,the brand or manufacturer of the item, and the source of where the itemmay be purchased. Other information about the content item 1102 mayinclude, for example, its quality and/or its material of fabrication.All of the examples provided for record data 1164 illustrate use of textand metadata associated or contained within the content item 1102 thatcan later be used to categorize the content item.

As described with an embodiment of FIG. 1 and elsewhere above, thecontent analysis system 1140 may comprise a “backend” of an overallsearch system. The content analysis system 1140 may include an imageanalysis component 1142 and a meta/text analysis component 1144 foranalyzing content items 1102 Content items 1102, in the form of webcontent, for example, containing images, text and metadata may beanalyzed for purpose of (i) classifying the object or objects in acontent item, (ii) generating data that is descriptive or representativeof global and/or local features of the object image in the individualcontent items, and/or (iii) generating signatures that providequantitative information about the appearance of the object in theimage, including of the global and local features. Additionally, one ormore embodiments provide that the content analysis system 1140 generatesor identifies text and metadata from content items 1102 or dataassociated with content items, for use in establishing text-based datathat serves as at least a part of a comparison or matching process in anoverall search algorithm. The text and/or metadata may include both (i)text/meta data existing in or with the associated contact record, and/or(ii) text/meta data generated from recognizing or analyzing images.

According to an embodiment, the content analysis system 1140 outputsindex data 1152 for index 1150. For a given object in an image, indexdata 1152 may include data that represents one or more of (i) theclassification(s) of a given object, (ii) global features of the object,(iii) localized segments of the object, (iv) local features of theobject, (v) text and metadata carried or associated with the contentitem from which the object in the image was located, (vi) text andmetadata generated from local features, global features or other imagerecognition/analysis results, and/or (vii) any combination thereof. Morethan one index 1150 may be used to store the index data 1152,particularly index data of different types. The index 1150 may bemaintained on a server-side system, using one or more machines.

With regard to at least some of the index data 1152, one or moreembodiments provide for quantitatively representing the local and globalfeature data in the index 1150. Once the image features (visualsignatures) are indexed, all the preprocessing steps are completed. Inone embodiment, the indexes are loaded to memory when the servers getstarted. In another embodiment, the indexes stay in the disk, and theyare loaded during the search time. In yet another embodiment, a cachecan be used along with one of the other embodiments. This cache wouldstore the most frequently used search results in the memory, and whensuch a search happens, it returns the results right away. If there isenough memory, then the cache can hold all possible search results. Inone implementation, one or more quantization algorithms can be used topack 4-byte floating point values to 1-byte to enhance storage and useof the index or indexes.

According to an embodiment, the index data 1152 that corresponds to theglobal and/or local features may be stored linearly, one after anotherin an index file (or database). Such an index structure enables asubsequent search to be performed by scanning all of the index dataitems one by one. Distances between individual index data items may becalculated one by one, and the results of the calculated distances arealso stored. Implementation of such a technique enables an optimalresult to be obtained, since every single item is considered duringsearch. However, without further modification, a search that uses theindex structure may take an unnecessary long time. In order to speed upthe linear storage and search, the index 1150 may divided onto multiplemachines. At search time, a search query is sent to multiple machines atthe same time. A reply may be formulated that incorporates the first Nresults back from each machine. The component providing the compilationof the results may reorder the results received from each machine.

In another embodiment, the features are indexed using other structures,such as spill trees, ball trees, vector quantization (based trees).These techniques store the features in leaves of a trees structure ofthe index 1150. Each node of the tree may serve as a decision mechanismfor focusing only a part of the inventory. Such a technique enables asearch to be performed faster than, for example, the linear techniquedescribed above. However, this techniques does not provide the exactsame result as the linear search. As such, there is a loss of accuracyin the results.

In another embodiment, a linear index is saved, but the search isexecuted in a cascade structure. The advantage of going linearly overeverything for comparison purposes is the guaranteed accuracy of thesystem. On the other hand, it is slow to compare to all of theinventory. As an alternative, the search can be executed within acascade. More specifically, fast features are used to run through thefirst pass on the index, and this produces a subset of the index, whichcan further be processed to match using more computationally expensivefeatures. The cascade can be constructed in multiple layers to speed upthe search as needed.

In another embodiment, the search results for all queries can be saved.If there are reasonably small number of search elements (e.g. 10million), a defined set of K (where K could be a few thousands) resultscan be saved for each query. This way, there is no calculation ofdistance values on the fly.

Further on the backend, procurement 1130 may include web crawlers thatcrawl various network sites for content items 1102. The type of contentitems 1102 that are sought may depend on the application for system1100. In one embodiment, system 1100 is used to enable visual searchingof merchandise, in which case procurement 1130 crawls (through crawlinput 1132) e-commerce sites. As an addition or alternative, theprocurement 1130 may crawl blogs, media sites and other network sites.In an e-commerce application, the procurement 1130 may seek contentitems that can provide a basis for a search for merchandise. Forexample, outfits worn by celebrities may be identified from pages onwhich celebrity gossip is provided. Other applications for system 1110include social networking and online dating. In such applications, thecontent items may correspond to pages that show persons, faces, orclothing, attire or apparel used by persons. Numerous other applicationsexist.

As an addition or alternative, procurement 1130 may also operate off ofcontent items that provide trigger input 1134. Trigger input 1134includes content items that are to form the basis of a search query, asspecified by users on remote network sites that are independent of thesearch system 1100. Such sites may, for example, contain images that anindividual may want to use as the basis of performing a search formatching merchandise items (e.g. a user may want to wear a dress thatlooks similar to one worn by an actress). As still another addition oralternative, procurement 1130 may also operate off of content itemssupplied by a user (e.g. through upload or other online submission). Forexample, a user may submit a digital photograph that is to be analyzed.Procurement 1130 may handle trigger input 1134 and user-input 1136similarly, in forwarding the image file data from those inputs to thecontent analysis system 1140. Rather than content analysis system 1140indexing the results of these inputs, pertinent portions of the analysismay be returned to a front end component for formation of a searchquery. The pertinent portions may correspond to an image that issegmented (from its original submission) to display an object, alongwith identification of its global and/or local features (eitherquantitative or as text), and possibly metadata or text provided orincluded in the submission.

On the front end of system 1100, user-interface 1110 may be coupled orintegrated with a search 1120 and executed from a server or server-sidesystem to an audience of terminals. In a network or Internetimplementation, user-interface 1110 includes a web page component thatis downloaded onto a terminal of a user. The web page component mayinclude functionality that enables the user to specify a search input1112. This search input 1112 may include (i) inputs of processed(previously recognized images), (ii) inputs of un-processed (neverbefore recognized) images, (iii) inputs that specify a global feature(e.g. primary color), (iv) inputs that specify a local feature (e.g.shape or pattern in a region of an object), (v) classification input,and/or (vi) text input of information determined from recognition andanalysis of images. Moreover, text searching and/or meta-data searchingmay be enabled using original text/metadata provided with the record.The search interface 1120 uses the inputs, either individually or incombination, to generate one or more search queries 1122 that areprocessed against index 1150. The index 1150 returns results 1154 thatidentify content records 1124 that satisfy criteria of the query 1122.The search module 1120 may retrieve the identified records 1126 from therecord data store 1160 and returns search result presentation 1128 thatis based on one or more of the identified records 1126. The searchresult presentation 1128 may, for example, be in the form of a web pagethat displays individual results as panels, each panel containing animage and optional text and/or metadata.

In one embodiment, search module 1120 is equipped to perform categoryspecific searches. These include searches where content records aresearched by classification. For example, in the case where record data1164 identifies category information from the content item 1102 or itscontext (e.g. manufacturer, brand, price or price range, quality,material of manufacturer), search module 1120 may be able to formulatequery 1122 to include the category or classification specification. Thecategory or classification 1122 may be combined with, for example, otheruse input (including image or text input), or alternatively provided asa stand-alone query. In this use, search module 1120 may use price,brand, or manufacturer, for example, to influence the search result. Forexample, when the user-input specifies a merchandise item, and thesearch yields multiple similar items in appearance, price of the itemsin the search may influence where and how individual items of the resultare made to appear to the user.

In addition to category searching, one or more embodiments alsocontemplate cross-category searching. More specifically, in the casewhere a user-input specifies one type of category (e.g. “shoes” in ane-commerce application), an embodiment may extend the visualcharacteristics of the specified category to other categories (e.g.“purses” or “shirts”) to return some results that match in appearance(if not category). Such an embodiment has application to e-commerce,where a user's desire to view, for example, clothing or apparel, offersan opportunity to suggest items from a different category that may alsobe of interest. In order to implement such an embodiment, search module1120 may simply submit two queries-one query that includes image data orother criteria combined with a category or classification, and anotherquery that excludes the category or classification (or submitsalternative category or classifications). Alternatively, the searchmodule 1120 may omit the category or classification when searching, anduse only image data or text input. Then on obtaining initial results,the search module 1120 may implement one or more filters to identifyitems by category or classification.

As an addition or alternative, in order to enable input of localfeatures, the user-interface 1110 may be configured to enable a user toselect or specify a portion of a processed image as the search input1112. The processed image may be existing, or it may be analyzed on thefly in the case of trigger inputs 134 and user-inputs 136. In eithercase, portions of the image may be made selectable, or at leastidentifiable with user input. Once selected, the portion of the imagecan form the basis of the query, or the user may manipulate or specifyadditional features that are to be combined with that portion. Forexample, the user may specify a portion of an object image, then specifya color that the portion of the object is to possess.

FIG. 12 illustrates a method for implementing a search system, such asdescribed with an embodiment of FIG. 11, according to one or moreembodiments of the invention. Accordingly, reference may be made toelements of a system such as described with FIG. 11 for purpose ofillustrating a suitable component or environment for performing a stepor sub-step being described.

In a step 1210, content items are identified for use in aggregatingsearchable data. The content items may include images, text andmetadata. One or more embodiments provide for the use of web content.Still further, one or more embodiments provide for the use of webcontent that corresponds to merchandise for sale, such as provided atnumerous e-commerce site. Such content items may be stored as recordsand include images of an item for sale, as well as descriptive text andmetadata associated with the file that is indicative or a classificationand source for the content item.

While embodiments described herein make reference to e-commerceenvironment and images of objects that are merchandise, otherembodiments have different applications. For example, an embodiment mayapply a method such as described herein, or with the system of FIG. 11,in the environment of an online social network. If such an application,objects that may be recognized included clothing, apparel, and faces.

Step 1220 provides that a procured content item is analyzed as part ofprocess to build searchable data. As described with an embodiment ofFIG. 11, data resulting from analysis of content items may be stored inan index that can be subsequently searched using user-specified searchcriteria. Multiple types of analysis may be performed on a content itemand that includes or carries an image.

In one embodiment, analysis of content items includes generation of asignature that represents a global characteristic or attribute of anobject in the image of the content item. For example, the globalsignature of a represent a dominant of primary color, color pattern,texture, shape or other physical characteristic that is descriptive ofthe object as a whole. When the content item includes images of persons,for example, the global signature may correspond to a recognition of aface of the person.

As an addition or alternative, one or more embodiments provide forgeneration of a signature that is representative of a local feature. Asdescribed with one or more embodiments, local features may include ashape, color, pattern, or the physical characteristic of a specificportion of the object in the image.

Still further, as an addition or alternative, one or more embodimentsprovide for generation or identification of text and/or metadata that isincluded in the content item, or otherwise associate with the contentitem. This text may be extracted, or generated as the end result of arecognition or image analysis process. In the latter case, for example,image analysis may determine that a caller of the object in the image isa hue of orange. Step 1220 may provide for the generation of the word“orange”, as a text descriptor of the content item, even though the word‘orange’ is not used or carried in the content item.

In step 1230, data and information resulting from the analysis of thecontent item is recorded. The information is recorded in a datastructure to enable subsequent searching, through use of queries thatcan contain one or both of text or image values or signatures. In oneembodiment, the data and information is recorded in an index thatcorrelates to a data store where the content items are stored inrecords.

Steps 1210-1230 illustrate to process of building searchable data usingimage analysis. This data may be subject to search by a user who canspecify as input text, images and visual values or attributes such ascolor. Accordingly, step 1240 provides that a search input is receivedfrom a user. This input may correspond to any of text input 1242, classor classification input 1244, feature selection 1246, image value input1247, processed image input 1248, and/or unprocessed image input 1249.Unprocessed image input 1249 may be followed by performance of imageanalysis as a sub-step 1253. Feature selection 1246 may correspond toselection of either a local or global feature, extracted from the imageof the object. Image value input 1247 may correspond to specification ofa physical and visual attribute, such as color, shape, pattern ortexture, independent of an image being displayed. For example, the usermay select items that are of the color orange. The image value 1247input may be made independent of other image information. For example,the user may specify a color for a watch, without having an image asinput. Alternatively, the image value 1247 may be inputted incombination with features or identification (e.g. through selection) ofanother image.

Step 1250 provides that a query is generated from the search input. Thequery may reflect any of the inputs described with step 1240. One ormore embodiments provide for the query to include multiple components(or queries) for different inputs, or combinations thereof.

In step 1260, the queries are processed against one or more indexes. Inone embodiment, different indexes or index structures are used to holddata of different kinds that result from the processing of the images.

In step 1270, a search result is returned to the user. The search resultmay include content that is based or derived from one or more contentitems that include data that match the criteria of the query. In oneembodiment, records from multiple content items are displayed andcombined on a single page for display to a user.

According to an embodiment, the presentation of the records may includepanels that contain both images and text/metadata from the matchingrecords. In one embodiment, a portion of the method may be re-performedbeginning with step 1240, using the presentation of panels as thestarting point. For example, the user may select a panel containing animage, in which case input in the form of processed image input 1248and/or text input 1242 may be used to form the search query. As anexample for an alternative or additional embodiment, the user mayspecify color, pattern, shape or other feature to be provided incombination (including as an additive combination) with an imagereturned in the result. The combination may indicate a user's desireditem, as modified by the image value. For example, the user may selectan items appearance, but use the image value to specify a differentcolor.

Front End Components of Search System

As described with an embodiment of FIG. 11, system 1100 may incorporatea user-interface that enables the user to specify various kinds ofinputs, and combination of inputs, for generating a query that cansearch processed image data. The user-interface 1110 and/or searchmodule 1120 may identify criteria from the inputs, and use criteria ingenerating a query that can be used as a basis for identifying itemsfrom an index that satisfy the criteria of the query. FIG. 13illustrates a user-interface for use with a search system such as shownand described with an embodiment of FIG. 11.

In an embodiment, a front end system 1300 of the search system 1100includes the user-interface 1110 (FIG. 11). The user-interface 1110 maybe comprised of a rendering component 1310, and a set of input handlingcomponents 1320. The front end system 1300 may also include a querygenerator 1330, which may form part of the search module 1120 (FIG. 11)and/or user-interface 1110. With regard to the user-interface 1110, therendering component 1310 displays content and functionality to the user.At least some of the functionality may be used to enable the user tospecify inputs (described as controls 1315 below) that are to form thecriteria for a search query. The set of input handling components 1320includes one or more of (i) an unprocessed image handling component1322, (ii) a processed image handling component 1324, (iii) a texthandling component 1326, and/or (iv) a class handling component 1328. Asan addition or alternative, an image value handling component 1329 mayalso be provided as one of the set of input handling components 1320.

In one embodiment, the rendering component 1310 displays a page 1311 tothe user. The page 1311 may include a panel presentation 1312, at leastin response to certain actions of the user. The panel presentation 1312may display a plurality of panels, each of which include an image andpossibly text and/or content or metadata. In one embodiment, the panelsof the presentation 1312 are selectable to enable the user to specifythe image and/or other content of the panel as a search criteria.

In addition to panel presentation 1312, the page 1311 may includecontrol input features 1315. In one embodiment, the control inputfeatures 1315 include a text field, an upload mechanism and one or moreuser-interface features for enabling the user to enter an image value.With the panel presentation 1312, and the control input features 1315,the user is able to specify as input (for a search term) multiple typesof inputs, including unprocessed image input 1342, processed image input1344, text input 1346, class input 1348, and image value input 1349.

Each selection of the panel may correspond to a processed image input1344, and is handled by the processed image handling component 1324. Theprocessed image input 1344 (for processed image handling component 1324)may also correspond to a selection of a portion of an object, and/orselection of an attribute or characteristic of a displayed and processedimage.

The un-processed image handling component 1322 may handle acorresponding an unprocessed image input 1342. This may occur when, forexample, a user-uploads or submits an image file to the procurement 1130and/or causing the image file input to be processed by the contentanalysis system 1140 for data from which the search query can be formed.As an alternative or addition, the un-processed image input 1342 mayoriginate from outside of the domain of the system 1100 and be detectedor received by components (e.g. procurement) that act as theun-processed image handling component 1322. For example, one or moreembodiments contemplate that a user can specify a content item, such asin the form of an image, on a third-party website and then have thatimage and the associated content analyzed by the content analysis system1140 (FIG. 11). Such an input may correspond to trigger input 1134 (FIG.11). In making the input, an implementation provides for code toexecute, from, for example, the user terminal or the server of thethird-party site. Execution of the code causes the content item to beforwarded to content analysis system 1140 for processing. As such, it ispossible for the un-processed image input 1342 to be made without theuser-interface 1110. For unprocessed image input 1342 (correspond totrigger input 1134 (FIG. 11) or user-input 1136 (FIG. 11)), one or moreembodiments provide that the content analysis system 1140 returns imagedata results 1343. In one implementation, the resulting image dataresults 1343 may be quantified and/or made use of similar to valuescarried by selections of processed images.

As described above, the processed image input 1344 may correspond to theuser entering selection input in association with an image that isdisplayed to the user. In one implementation, the user may view a pagecontaining numerous panels, and make a selection of one panel overothers. This selection may correspond to the processed image input 1344.The processed image input 1344 may also specify global features (e.g.dominant color) or local features through the selection input.

The control features 1315 may be used to enter image value inputs 1349,such as color, pattern, or shape. These values may also correspond tolocal or global image features, but they may be made independent ofand/or additive to a displayed image. For example, the control features1315 may be used to specify a color for an object that is identified bytext input 1346, or the control feature may be used to modify aselection input corresponding to the processed image input 1344. Asanother example, a user may specify a panel or a portion of an image ina panel, then specify an alternative value of one of the attributes ofthe selected image (e.g. color) through use of one of the controlfeatures 1315. As another example, the user might specify text input inaddition to an image input. For instance, a set of text drill downs canbe presented at the side of the panel, and the initial results set canbe further filtered using the text entry through drill downs.

As another example, the dominant color may be specified through a colortool that enables a user to specify a desired color on a spectrum.Similar tools may be provided for patterns, shapes and even textures.Additional or alternative features are described below with otherembodiments. As an addition or alternative, one or more embodimentspermit the user to specify text as a global feature.

The user may specify classification input 1148 through any one of manyways. For example, the user may enter text, select menu fields orspecify or select an image. The classification input 1148 may be basedon the text or menu item, or based on a classification of the object ofthe selected image. The class input handle 1328 may make a classdesignation that correlates to the classification input 1348.

As described with one or more embodiments, a user may enter text input1346 in addition or as an alternative to the other types of input. Oneor more text fields may be included with the interface 1110 to enablethe user to submit text. The searchable text of the index of other datastructure may include text that is characteristic of an image, andprogrammatically determined from performing analysis or recognition ofthe image. For example, searchable text may include the primary colorsof the object in the image, in the form of text e.g. “orange”).

The query generator 1330 may convert inputs from any of the componentsof the user-interface into one or more queries 1382 for differentindexes 1150 (FIG. 11). Individual queries 1332 to one index 1150 (FIG.11) may combine inputs of different kinds. Specifically, one or moreembodiments provide that any of the different kinds of inputs describedabove may be generated into one or more queries by the query generator1330. For example, the user may specify the processed image input, orcontrol features 1315 for color and text input. As another example, theuser may select a panel image (processed image input 1344) and a colorinput (control 1315) to specify a desired object image and color. Thequery generator 1330 may be used to generate a query 1382 for differentindex sources. Different components of the search module may usegenerated queries 1382 to perform comparisons and matchings of values inthe index(es) 1150.

Visual Similarity Searching

One or more embodiments provide that image data searches may beperformed through use of indexes or similar data structures that enablequantifiable comparisons of values representing image attributes orappearances. In performing a visual search in which the user's input isimage data, the may be required to submit or specify a visual sample. Inone embodiment, the visual sample corresponds to an object image of theuser's preference or specification. A similarity search may be performedusing that image as the search input. The similarity search attempts toidentify a set of images that are similar to a specified image.

FIG. 14 illustrates a technique for enabling and performing a similaritysearch using an image input, according to one or more embodiments of theinvention. A technique such as illustrated with an embodiment of FIG. 14may be implemented with, for example, the system 1100 of FIG. 11.Accordingly, in describing a technique of FIG. 14, reference may be madeto system 1100, or components thereof, for purpose of illustratingcomponents for performing a step or sub-step being described, oralternatively for using a result of the step being described.

In one embodiment, a step 1410 establishes image similarity data forsubsequent searches, in which techniques used to implement imagesimilarity measurements are at least initially determined from humanjudgments. Similarity distances are employed in content-based imageretrieval (CBIR) systems in order to retrieve images similar touser-provided queries. Implementing methods measuring image likeness isa challenging task as perceptual similarity is often subjective andtask-dependent. For example, a criterion for measuring similarity in animage taken by a consumer level digital camera in a natural environmentis different from the criterion of similarity for professional andstaged images of products or merchandise such as jewelry.

Step 1420 provides that similarity amongst images or image portions maybe quantified relative to a set of assumptions using distancemeasurements. Several distance functions have been used in the past tomeasure the similarity of images with respect to specific visualattributes. The specific visual attributes include color distribution,texture, or object shapes. One or more embodiments described includesome of these distance functions, as well as features in the precedingsections. An embodiment described includes methods that translate suchlow-level feature distances into a measure of perceptual similaritycapturing the intent of the user search input. The search input may beprovided from the user through, for example, execution of theuser-interface 1110 (FIG. 11) and search module 1120 (FIG. 11). One ormore embodiments provides for computer-learning of these distancefunctions from a training set of user similarity judgments.

Step 1430 includes attribute matching, for purpose of measuringsimilarity of images with respect to a specified visual attribute (e.g.color). The output of each attribute matcher is a numerical valuemeasuring the distance (dissimilarity) between two images with respectto a specific visual feature. Such measure are termed as “featuredistance”.

One or more embodiments include methods to map a set of featuredistances into a single perceptual similarity measure. This computationmay be cast as a regression problem in which the goal is to predict theperceptual similarity values from a set of observable feature distances.As mentioned earlier, such a similarity measurement may embody alikeness criterion that is dependent on the purpose of the imageretrieval task. This regression model may be modeled after humansimilarity observations reflecting the user intent.

In one embodiment, perceptual observations are collected by askingsubjects to quantify the degree of perceived similarity of images, forexample by providing likeness values between 0 (opposite) and 1(identical). Rather than requiring subjects to inspect image pairs atrandom, in this document one or more embodiments include an approachaimed at speeding up or enhancing the perceptual data collection. Sincesimilar images are few in number as compared to images that aredifferent from one another, the process of acquiring human judgmentswith high value of likeness is generally more time-consuming than thetask of collecting dissimilar images. One goal may be to use the featuredistances to identify image pairs likely to be declared as similar bythe subjects. Specifically, for the purpose of collecting observationsof highly similar images, only pairs having small feature distances areshown and evaluated by users. The highly similar images may be displayedto the user in the form of panels, cascaded so that numerous similarimages are displayed at once. Similar to the techniques discussed withmanual enrichment, the cascade manner in which panels are presentedenables the use of manual feedback and input from operators to movequickly.

In another embodiment, users might define partial orderings over sets ofimages with respect to similarity to given queries. In one embodiment,this framework can be used to combine multiple feature distances. Inanother embodiment, a perceptual mapping from each feature can be madeseparately by using this framework.

Several machine learning algorithms may be applied to learn similaritymeasures from the sets of perceptual observations. In its simplest form,a linear least square regression can be used to learn a linear functionof feature distances predicting the similarity distances from thetraining set of perceptual values provided by the users. In anotherembodiment, a logistic regression can be employed instead of a linearone. One of the advantages of logistic regression is that it canrestrict the output values to any desired range, while traditionallinear regression is unable to do so. More powerful prediction modelscan be obtained by applying the regression algorithms tohigher-dimensional feature vectors computed from the original featuredistances. For example, polynomial kernels can be used to computenonlinear features of the inputs. Classification algorithms can also beused to estimate similarity values from the feature distances. Theuser-provided perceptual judgments ranging in the interval [0,1] can beinterpreted as probabilities of membership to classes “similar” or“dissimilar”. These probabilities can be used as input to train binaryclassification methods, such as support vector machine or boosting.

In another embodiment, a weighted combination of the feature distancesare used. For a query image (Q) and a database image (D), one or moreembodiments use the following formula to measure their overalldissimilarity:TotalDistance(Q,D)=sum(Distance(Q,D,i)*w(i)), i=1, 2, . . . , N  (1)where N is the number of features, Distance(Q,D,i) is the Feature i'sdistance between Q and D. w(i) is the weight assigned to Feature i.

A determination may be made as to weights w(i) that can be applied tothe distance function. Such a determination may be made as follows.First, distance vectors for both similar image pairs and dissimilarimage pairs are calculated. “Similar” and “Dissimilar” may be defined bya given set ground-truth data, Human operators may be used to determineif a pair of images are relevant to each other or not.

For similar image pairs, a target function is 0 for distance vector:[Distance(x,y,1),Distance(x,y,2),Distance(x,y,3), . . . ,Distance(x,y,N)]

For dissimilar image pairs, a target function is 1 for distance vector:[Distance(x,y,1),Distance(x,y,2),Distance(x,y,3), . . . ,Distance(x,y,N)]

From this determination, weights w(i) may be optimized by making thevector value TotalDistance(x,y) sufficiently close to the target for theselected distance vectors. In one embodiment, Linear DiscriminantAnalysis (LDA) can be used for this purpose.

As it is time consuming to calculate the distance between the queryimage and the each image in the database for all the features, one ormore embodiments provide for a technique that uses one primary featureto identify an initial candidate set (for example, 10% of all the imagesin the database), and then use all the features to do a final match. Inthis way, an embodiment can perform the matching at a faster rate, withvery little accuracy loss.

FIG. 15A illustrates an example of a text search result on shoes. Theuser can then choose one of the shoes and run an image similarity(likeness) query on it.

In contrast, FIG. 15B illustrates an example of similarity (likeness)search results on a shoe. The results match the query image with acombination of color, shape, and texture.

User-Interface Controls

One aspect of searching images with image input (or content based imagesearching) is that results can significantly vary as between userexpectations and programmatic implementation. Accordingly, one or moreembodiments recognize that user-feedback, responsive to search resultswith image criteria, can provide a valuable resource in enabling a userto specify a desired visual characteristic of an object. As such, one ormore embodiments provide for use of feedback mechanisms that can be usedby individuals to specify color, shape, pattern or other image valuesthat are characteristic of an object's appearance.

In one embodiment, a user's search result is accompanied by variousmechanisms to that enable the user to provide feedback. Graphicuser-interface features for providing feedback may include any one orcombination of (i) sliders, (ii) color pickers, (iii) location regionselection, and/or (iv) drill downs.

A slider is a graphic-user interface feature that is moveable by theuser in a linear fashion to enable a user to specify a value between aminimum and a maximum. One or more embodiments enable a user to use oneor more sliders 1610 in order to select color (e.g. by presenting colorvalues for one or more hues in a spectrum linearly), shape or a patternas a characteristic of focus in providing a result or modifying anexisting search result. An example of a slider is shown with anembodiment of FIG. 16.

A color picker enables the user communicate a specific color for purposeof receiving a result or modified search result that contains objects ofthe selected color. An example of a color picker 1710 is shown with anembodiment of FIG. 17.

The local region selection enables the user to specify input thatselects a portion of an image of an object as input. As a result of theinput, the system focuses processing (e.g. query generation andmatching) on the selected part of the image. A resulting search resultmay achieve results that are similar to the particular region. Anexample of a selector graphic feature 1810 for enabling the user to makethe local region selection is shown with an embodiment of FIG. 18. Thefeature 1810 may be graphic, and sizeable (expand and contract) over adesired position of a processed image 1820. In the example provided, arectangle may be formed by the user over a region of interest of theimage—that being the face of a watch. An accompanying text 1812 explainsuse and operator of the feature 1810. Results 1830 include items of thesame class, with the localized selected feature being considered similarin likeness of or as a match.

A drill down feature enables the user to provide pre-formulated textqueries or classification identifiers to filter or modify a searchresult. For instance, the user can specify text input of “open toe” forthe object of a shoe, and an existing search result may filter to removecontent items that are processed as being “open toe”.

With regard to sliders, one or more embodiments provide for use ofsliders in the client-side relevance feedback (RF) for a system thatenables content-based image retrieval, such as described with anembodiment of FIG. 11. Graphic user-interface features such as slidersallows the end user to continuously change key parameters by adjusting aposition of a bar or similar graphic feature. The position of the barconnotes a value, such as proximity or similarity in appearance.Manipulation of sliders enables a search result to be modified. In oneembodiment, the modification is responsive and almost instant inresponse to the bar or feature being provided a new position. Comparedwith conventional server-based relevance feature mechanisms, one or moreembodiments enable the end user to more easily find desired images whilereducing computation load on a central server or computer system.

One or more embodiments further recognize that image searching systemssuch as described with an embodiment of FIG. 11 can have difficultyreturning the best or most relevant search results with just a singlequery image or input form the user. For example, a user may beinterested in the color, the texture, a certain object, or a combinationof multiple factors. One or more embodiments enable the user to interactwith a system and its search results, in order to enable final searchresults which are closer to what the user has in mind.

Furthermore, one or more embodiments contemplate that in a network orclient-server architecture, a user's feedback parameters on a clientside terminal (usually a Web browser now) result in the feedbackparameters being communicated to a remote server. In such anenvironment, a remote server reprocesses a query based on the feedbackand sends back an updated search results for the client terminal. Suchround trip on a network may include latency issues, which may limit orhinder the user in providing the feedback to the search results. In oneembodiment, together with the initial search results, the server alsosends additional information to the clients. In the current embodiment,the additional information includes the distances between the queryimage's individual features and the selected nearest neighbors'individual features.

The user may then change key control parameters. Based on the additionalinformation cached at the client terminal, the query results can be veryefficiently updated at the client side with limited or no involvement ofthe remote server. The update speed can be fast enough to supportseemingly continuous parameter change and corresponding instantaneousquery result updates, so that the user can more readily view a mostrelevant search result.

In one embodiment, for the initial query, the system uses LinearDiscriminant Analysis (LDA) to combine many image features (as describedunder feature extraction) using weighted summation.Distance(Query,Example)=sum(weightI*DistanceFeatureI(Query,Example)),I=1, 2, . . . , N

Statistically, it has been shown that the weights lead to very goodaccuracy on general images. However, as expected, the weighting is notoptimal for all images.

FIG. 19 shows an example of how image features may be combined in aquery, under an embodiment of the invention. In an example shown, anoriginal query results in a cat image. Due to the weighting on colorfeatures, only two cat-related images appear in the 48 panels thatcomprise the search result. An embodiment performs relevant feedback asfollows:

-   (i) Collect the DistanceFeatureI(Query, Example) for the first K NNs    of the original query results. So here is K*N distances.-   (ii) Group the features into multiple categories: color features,    shape features, pattern and metadata features, etc. For each example    in the K NNs, multiple distances are determined that correspond to    color, shape, texture and metadata respectively.-   (iii) Allow the user to almost continuously change the weighting    between the distances by the use of sliders. The new NNs are    selected according to the new slider weights:    New Distance=w1*ColorDistance(Query,Example)    +w2*ShapeDistance(Query,Example)    +w3*TextureDistance(Query,Example)    w4*MetadataDistance(Query,Example).

Where w1 through w4 are the weights obtained through the sliders. Theresult after increasing the pattern features are provided in FIG. 20. Itcan be observed that many other cat images are included in the results.

With further reference to FIG. 16, another example is shown of how imagefeatures may be used or quantified in a query, according to one or moreembodiments of the invention. In an embodiment shown, the user views aresult 1620 comprising similar shaped objects having different colors.This is a result of the color slider 1610 being minimized. By reducingthe weight of the color, the user is able to get similar shape andpatterns.

With further reference to FIG. 17, a color picker relevance feedbackmechanism may correspond to a user-interface feature that is used togive the user the ability to choose a particular color. Once the userchooses a color, the color features of the query image is swapped withthe color of the picked color. Then a global image similarity search isapplied on the database. In one embodiment, a weighted combination ofthe user selected color and the query color are used. The result 1720 isof items that have a same or similar classification, and a global colorfeature that is matching or most similar.

The color picker 1710 search may thus focus on shape, texture and themetadata features from the original query image, while swapping orreweighting the color features with the color that the user has picked.As a result, the user may receive items with similar shape, pattern andmetadata to the query item, but with the color that the user hasselected. FIG. 17 provides an example where blue was selected by theuser. The shoes start matching the query image in all aspects but thecolor, and the color of the returned query items are blue.

As described, a local search relevance feedback mechanism corresponds toa graphic user-interface feature that enables a user to select a regionof interest from a processed image, under an embodiment of theinvention. The processed image may be one that is provided or returnedwith a search result. As described in the Feature Extraction Section,the features for local regions are calculated prior to search time. Whenthe user selects the region, the system uses the features extractedspecifically around that region. Then the matching happens using theselected region's features. In one embodiment, only the local featuresare used for matching. In another embodiment, a weighted combination ofglobal and local features is used. Still further, a cascade of features'matching can be used. For example, the first pass is made using some ofthe global features, and the second pass can be made using the localfeatures. With further reference to the local feature search shown inFIG. 18, an embodiment illustrates a scenario in which watches withthree chronographs are returned as a search result.

With regard to drill down relevance feedback, metadata is essential forallowing users to narrow a similarity search. The user interface canallow the user to select a particular buzzword (as described inpreceding sections), or brand of an item, and filter the result set toonly those items containing the buzzword. The buzzword sets describedpreviously in this section can be employed to group the relevantbuzzwords, and display them more intuitively on the screen. In addition,only buzzwords related to those that are present in the query item canbe shown to un-clutter the interface. Using these text drill-downs, theuser can then limit a similarity search result to the results that onlycontain a particular word or phrase.

E-Commerce System

Any of the embodiments described herein may have applications toelectronic commerce. More specifically, with reference to FIG. 1, one ormore embodiments provide for the use of an image analysis system inwhich content items include commercial content containing images ofmerchandise and products for sale. E-commerce content items includerecords stored at online sites. Such records are often assembledon-the-fly, in response to search request. Generally, commercial onlinecontent provides for records that have images, text, and links to a siteor to other content items.

FIG. 21 illustrates a method for implementing an e-commerce system usingany combination of embodiments described herein, according to anotherembodiment of the invention. A method such as described with FIG. 21 maybe used to in connection with a system for analyzing content itemsand/or using images or image data or values, for purpose of assistingindividuals in identifying content items for merchandise. Specifically,an embodiment such as described enables an individual to use imagesearching and analysis in a commercial environment. As such, a methodsuch as described with FIG. 21 enable an individual to search formerchandise using visual characteristics and appearances of the objectof interest.

In embodiment, FIG. 21 may be implemented on a system such as describedwith FIG. 11 (using components and elements described with other figuresherein). Reference to elements of FIG. 11 may be made for purpose ofillustrating a suitable element or component for performing a step orsub-step being described.

In a step 2110, content analysis system 1140 processes content itemsthat carry images and description of merchandise. Examples ofmerchandise include clothing (shirts, pants, hats, jackets, shoes,ties), apparel (purses, sunglasses), carpets, furniture (sofas, lampshades, furniture covers), and watches and jewelry. Content items may behosted or provided at other sites, including online commerce sites, andauction sites. The content items may include one or more images of thecontent item, as well descriptive text and/or metadata of the item forsale. Typically (but not always), images provided for an item for saleare focused on only the particular item, and do not carry non-relatedobjects.

Step 2120, various kinds of search inputs are enabled for a user'ssubmission. As described with, for example, an embodiment of FIG. 13, auser-interface 1110 (FIG. 11) may enable search input from a user tospecify text, un-processed images, processed images, image featurevalues such as color, classification input, or a combination thereof. Aspart of enabling the search inputs, the index 1150 may store index datathat holds the analyzed images of the merchandise content items in asearchable form.

Step 2130 may be performed at run-time. In step 2130, one or morequeries are generated from the search input. One or more queries mayinclude one or more criteria that is specified by the user.

Step 2140, record information is searched based on a criteria identifiedin the query. In one embodiment, the index 1150 is searched for contentitems that satisfy the criteria of the query.

In step 2150, a search result is returned in the form of a presentationof panels. Each panel in the presentation may include a processedcontent item, containing a processed image and/or accompanying text forthe content item.

With the return of the search result, one or more embodiments providethat the user may either (i) filter, modify or drill down the searchresult (step 2160), or (ii) view a content item in more detail (step2170). With regard to step 2160, the user may be provided user-interfacecontrols in the form of a slider (for color, pattern, shape etc), colorpicker, local region selection, and drill-down features. The featuresmay be used to refine or modify the user's search, in a manner such asdescribed with embodiments provided above.

With regard to step 2170, the user may carry steps out to access thesite or network location where the item is for sale, and purchase theitem from the search result.

FIG. 22 illustrates a record corresponding to a processed content itemhaving data items that are determined or used in accordance with one ormore embodiments described herein. A record 2210 such as shown may bestored in, for example, the record data store 1160, for use with frontend system 1300. In an e-commerce application, record 2210 may includeinformation processed, carried and/or derived from a content itemrelating to merchandise.

In an embodiment, record 2210 includes a processed image 2212 that hasundergone recognition or processing, such as provided by system 100(FIG. 1) or content analysis system 1140 (FIG. 11). The processed image2212 may be selectable to be enlarged, and portions of the image may beselected apart from other portions for purpose of enabling localizedsearching. The record 2210 may also include a set of analyzed text andmetadata 2220, which may be derived from, for example, the global orlocal signatures, the source metadata, the descriptive text providedwith the unprocessed content item, and the processed image.

Source information 2230 may also be included in relatively unanalyzed orunmodified form. This information may carry or include the brand, theclassification (as identified at the source), the price, an originalproduct description, a merchant link and/or one or more product reviews.

Information from record 2210 may be displayed as part of a resultpresentation to a user. The user may select the record (by selecting thecorresponding panel from the presentation) and either (i) modify, filteror redo the search result, or (ii) select to view the information aboutthe merchandise represented by the content item. In the latter case, theuser may select the link to view the content item at the source or atthe site of the merchant, where the product can be purchased. In orderto modify or filter the search result, the user may select a portion ofthe processed image 2210, or input text, or input one of the image valuecontrols that modify visual/physical attributes displayed with theprocessed image 2210.

Network Retrieval

While some embodiments described herein provide for analyzing images ofcontent items and storing the determined information in a searchablesystem of records, embodiments described herein provides for processingand use of images that are not inherently for use in commercialtransactions, and are created with alternative purposes (such as toentertain or to inform). Such websites and content may be outside of thecontrol of the system where image recognition analysis is performed.Unlike e-commerce sites, the contents of such web pages may lackconventional standards or rules. Moreover, there may be much randomnessin what content is displayed on such websites or with the web content.

Accordingly, one or more embodiments may be applied to content itemsthat are retrieved from web pages or other web content, where littleadvance information is known about the contents of the webpage orcontent. For example, embodiments may retrieve content form websitesthat provide news or gossip about persons of general interest to thepublic. As described, images from such content may be pre-processed orprocessed on-the-fly by embodiments described herein to enable users tospecify an image or a portion of an image as a search criteria. Inparticular, an object of the image or portion thereof may be specifiedas search criteria for searching for similar merchandise items. Rules ofsimilarity or likeness searches may be applied.

According to an embodiment, one or more remote web pages are accessed toretrieve content on display. Information is determined about an objectshown in a am image of the content on display. At least a portion of theobject shown in the corresponding image is made selectable, so that itsselection is associated with the determined information. A selection isdetected of at least the portion of the object in the image, using thedetermined information.

In another embodiment, a system is provided that includes a retrievalcomponent and one or more modules. The retrieval component istriggerable by activity at a remote web page to retrieve content ondisplay at the remote web page. The one or more modules are configuredto identify one or more objects in the one or more images of the contenton display. The modules determine whether the objects are of a classthat are designated as being of interest.

Still further, another embodiment provides for accessing a remote webpage to retrieve an image on display. The image is analyzed to detect aperson shown in the image. One or more of a clothing, apparel, orjewelry worn by the person shown in the image is identified. A portionof the image corresponding to that detected object is then madeselectable, so as to enable a person viewing the web page to select, forexample, an item of clothing from a person appearing in the image. Withselection of the portion of the image, information may be displayedabout the item, including information determined from a visual search ofthe item. While such an embodiment is specific to items related to aperson (e.g. clothing), numerous other kinds and classes of items may bedetected in the alternative. These include, for example, items of homedécor (e.g. furniture, carpets, drapes, utensils or dishware), machineryetc.

Furthermore, an embodiment such as described may include numerouse-commerce applications. For example, clothing on an image of acelebrity may be segmented and made selectable when the image isdisplayed on a blog. Viewers may select the clothing from the image, andsee (i) a link or information where the item or an item closelyresembling it can be purchased, or (ii) have a visual search performedthat compares the characteristic of the selected item to other items ofa same class.

With embodiments described herein, the term third-party or remote meansa person, party or location that is outside of the domain oradministrative control of another system or entity.

FIG. 23 illustrates a method for using remote web content for purpose ofidentifying search criteria for performing an image search orcombination search, according to one or more embodiments of theinvention. A method such as described with an embodiment of FIG. 23 maybe performed using modules and components described with otherembodiments of this application. Accordingly, reference may be made tosuch other modules or components for purpose of illustrating a suitablecomponent for performing a step or sub-step.

In an embodiment, a step 2310 provides that images on a page of a remotesite are analyzed or inspected for objects of interest. In anembodiment, objects of interest correspond to merchandise items, such asitems of clothing or apparel. More specific examples of items ofinterest include shirts, pants, shoes, necklaces, watches, ties, skirts,dresses, hats, purses and backpacks. Numerous other items may alsocorrespond to objects of interest, including, for example, housewareitems (dishes), plants, animals and people.

Step 2320 provides that identified portions of images that aredetermined to be objects of interest are made active. Active imageportions of an image may act as visual links. A person may view theimage and select an entire object of interest by selecting a region ofthe object (e.g. through movements of a computer mouse or pointer).Active image portions detect selection input, or partial selection input(such as when a pointer hovers over a link). The web page or otherresource provided with the viewing of the page may associate anoperation or set of operations with the selection (or pre-selection).

In step 2330, selection of an active image portion results in a querybeing submitted to a visual search engine. The visual search engine maycorrespond to any image search system, such as one described with anembodiment of FIG. 11, configured to handle content items submittedon-the-fly. The query may identify the active and selected portion ofthe image.

In one embodiment, the image portion that is selected is pre-designatedan identifier that is provided in the query. In another embodiment,features of the active image are identified from data stored with thepage or elsewhere. These features are then used in the query. Stillfurther, another embodiment provides for use of a visual signature ofthe image portion, which may be provided in the query. The signature maybe provided on the web page or elsewhere. For example, the image portionmay be associated with an identifier that is used to retrievepre-determined image signatures or features, then the signature/featuresare included in the query. In the case where the web page ispre-processed (see e.g. embodiment of FIG. 24), one or more embodimentsprovide that the query simply identifies the image for the backendsystem (e.g. content analysis module 1140 of FIG. 1) Data for performingthe search may be performed by retrieving identifiers of the image(using the image identifier) on the back end.

Still further, one or more embodiments provide that features orsignatures of the image are determined on the fly-in response to theselection action determined from the user. Such an embodiment mayutilize, for example, modules of system 100, such as the segmentizer 110and feature extraction module 120.

In addition to using data from or associated with the image, one or moreembodiments provide for the viewer to submit additional data thatformulates the query. In one embodiment, the viewer can select an imageportion (e.g. a shirt). In response, software enables the user tospecify additional information, such as through a software-generatedwindow on which the user may enter text. The user may seek to providetext that describes or classifies the object further (e.g. “woman'sblouse”). As an addition or alternative, the user may specify color,either visually or through text, if, for example, the viewer isinterested in the item but would prefer another color. For example, anyof the user-interface features described with embodiments of FIG.15A-FIG. 19 or elsewhere may be provided to weight one or morecharacteristics of the image being displayed, or to otherwise supplementor modify characteristics of that image.

As an alternative or additional embodiment, the query may also identifythe domain from which the user viewed the images.

In response to the query, step 2340 provides that the visual searchengine returns images of objects that correspond to or are otherwisedetermined to be similar in appearance or design or even style, as theobject of the user's selection. According to an embodiment, the resultmay display images that are linked to e-commerce sites or transactionlocations. Thus, if one of the results interests the user, the user mayselect the result of interest and be directed to an e-commerce site orlocation where he can purchase the item provided with the result.

As described with, for example, an embodiment of FIG. 11, the visualsearch engine may maintain a database of images, where images in thedatabase have the following information stored: (i) a URL to identifywhere the image comes from (e.g. an e-commerce site), (ii) a visualsignature resulting from, under one embodiment, a separate recognitionprocess, (iii) metadata, including descriptive text. The visualsignature may be in the form of a class-specific signature. More thanone signature may be maintained. As described with, for example, FIG.7-10, the signatures may include data that describes or representsglobal or local features (including color, pattern, and shape). Metadatamay be programmatically or manually generated based on color, shape,type of object or other descriptive markers (e.g. manufacturer of a pairof shoes).

When the query is received, the identifiers provided or identified byquery are referenced against the database to determine the searchresult. The identifiers may include signatures of the object identifiedby the image portion, either provided in the query or determined throughdata contained in the query. The text or other features provided by theuser at the time of selection may be referenced in the database as well.Under one implementation, some weighting or rule based algorithm may beused to value text entry with the image signature of the selectedobject. For example, user-specified text entry may be used as a requiredqualifier, and the visual signature of the selected image may be used tosearch in the class of images that satisfy the qualifier.

As an addition or alternative, information about the domain from whichthe query was generated may be used to further refine or specify thecriteria of the query. For example, certain domains may be known to beof interest for a particular type or class of objects (e.g. shoes andhandbags). When the query is generated from that domain, the visualsearch engine may map the domain to a set of criteria (e.g. textcriteria) for use with that domain. As with user-submitted additionaldata, the criteria determined from the domain mapping process may beused as part of a weighted or rule based algorithm.

As described with FIG. 24, one or more embodiments provide that theobjects of interest correspond to apparel or clothing of persons. Thefollowing provides an example. A user may view a pair of shoes worn by acelebrity at web page and be interested in the shoes. Embodimentsdescribed with FIG. 1 and elsewhere in this application enable theperson to select the portion of the image that has shoes (the object ofinterest). Once selected, a search result is provided to the user basedon characteristics or appearance of the shoes. In one implementation,the search result may be of shoes that match or are similar inappearance or otherwise to the shoes of interest. However, in otherembodiments, the search result may return different objects that have asimilar appearance or style to the selected object of interest. Forexample, some or all of the search result may include a blouse or shirtthat has a similar pattern or color as the shoes of interest.

FIG. 24 illustrates a back end process for activating images on a webpage, under an embodiment of the invention. The backend process may beimplemented using components, modules and techniques described withother embodiments, such as with embodiments of FIG. 1. Each of theprocesses described in an embodiment of FIG. 24 may be performed bycorresponding components or modules, or incorporated in functionalitydescribed with one or more embodiments described elsewhere in thisapplication.

Initially, a retrieval process 2410 is performed to retrieve images of awebpage for analysis. The retrieval process may be programmatic, manual,or a combination thereof. A webpage at a given network location or sitemay be accessed, and its images identified. In one embodiment, a triggerinput 2412 is provided to cause the retrieval process to be performedfor a web page.

In one embodiment, the trigger input 2412 is provided by a webpagesubscribing to a service implemented in association with the retrievalprocess. As part of the process, an operator of the webpage may employ ascript file that is incorporated into the web page or in connection withan object on the web page. In this way, the script file may serve as atriggering component. Each time the web page is downloaded by a user,the script file signals the retrieval process 2410 from another networklocation. The retrieval process 2410 may check the web page for newcontent, and extract images that are newly uploaded. As an alternative,a schedule input 2414 may be used to implement the retrieval processprogrammatically on remote web pages.

The result of the retrieval process 2410 may correspond to image files2416 that are inactive or unprocessed. The image files 2416 may besubjected to an image classification process 2420. In general, the imageclassification process seeks objects in images that are of a particularkind or type. For example, in one implementation, image classificationprocess 2420 seeks items for which corresponding merchandise isprovided, such as apparel, clothing and jewelry.

According to one embodiment, image classification process 2420determines some initial information about the image as a whole in orderto determine (i) whether the image file 2416 is a good candidate forenabling active portions, and/or (ii) objects of interest that arecandidates or otherwise likely to be in the image

The image classification process 2420 recognizes that images on webpages are often candid, or freeform in nature. Such images are typicalin various news and celebrity websites. Such images often do not followrules of convention, as present normally in, for example, e-commercecontent items. For example, some images may be close-ups of persons orportions of persons (e.g. stomach of a celebrity), others images may beof a celebrity from far away, another image may contain a body part likea hand or foot. Some images are bad candidates for activation. Forexample, a person may be occluded by objects that are not of interest,or the perspective of the image may be such that no data is usable onthe image.

In an embodiment, classification of the image file determines theperspective, and whether the image contains a person or a portion of aperson. If a person is in the image, the classification of the image maydetermine what portion of the person is of focus in the image. In orderto determine perspective and whether a person is in the image, aprogrammatic process may scan pixels or image elements for features thatare markers. In one embodiment, markers correspond to the face of theperson, as identified by, for example, eye brows, the eyes, the cornerof a persons mouth, the lip shape, the tip of the nose, a person's teethor hairline, a persons thumb or finger nail (for hand-shots inparticular) or a person's toes, ankles or toe nails (for a foot shot).Different markers may be identified with different levels of certainty.See priority application U.S. patent application Ser. No. 11/246,742,entitled SYSTEM AND METHOD FOR ENABLING THE USE OF CAPTURED IMAGESTHROUGH RECOGNITION, which is incorporated by reference herein. In oneembodiment, face and face feature detection techniques described theremay be used to detect the presence of a face in an image. From the face,other aspects of the person shown in the image may be detected, such as,for example, the person's clothing, apparel or jewelry. The face, orfeatures thereof (e.g. yes) may thus serve as a marker for enablingprogrammatic detection and identification of other aspects (e.g.clothing) of the person in the image. According to an embodiment, onceone marker is located, the presence of another marker may confirm thedetermination that both markers are present. For example, if a person'seyes are present, a program may then estimate, from the size of theregion where the eyes are, a position of the eye brows, nose tip ormouth corner.

Once the person is identified, an embodiment provides that a candidateset of clothing or apparel is identified, corresponding to what clothingare apparel may reasonably be expected to be present in the image. Forexample, a headshot of a person may result in the determination that theimage of the person may contain a hat, or a shirt, but that there willbe no shoes or pants in the image.

As part of the image classification process, images that are poorcandidates for activating portions are identified and marked to not beanalyzed. These images may correspond to cases where there isblurriness, occlusion of objects or issues of perspective or angle ofviewpoint of the image.

The object classification process 2420 may also use other information orclues on the page to determine the nature of individual images on thepage. For example, text caption accompanying an image may be analyzed todetermine whether the image contains male, female or both. Femalepersons in images may restrict the classification of apparel or clothingto that of women's wear. Other clues may be based on the web page itself(e.g. web page with known female audience). In one embodiment, theobject classification process 2420 may be performed by objectexterminator 140.

A process 2430 analyzes the image to determine what apparel or clothingis present (“apparel identification process 2430”). The analysis may beperformed to determine if the pixels or image data reflect the presenceof apparel or clothing that is one of the candidates identified fromprocess 2420. In an embodiment, the markers are used to estimate thepossible location of an apparel or clothing from the candidate set. Theestimated position may reflect the size of the image, and the sizeand/or posture of the person in the image. Based on the positionestimate, the image analysis may determined, for example, color valuesof pixels or image elements at the region to determine a color orpattern that is consistent with the existence of the candidate item ofclothing or apparel. Once such a point is found, the image analysis mayscan in a direction that is towards, for example, the markers of theperson's face. A transition into a color that is known to be in therange of skin may reflect a boundary of the clothing or apparel. Theboundary may be followed to determine a perimeter of the identifiedclothing or apparel.

Other apparel from the candidate set may be determined in similarfashion. For example, the marker may be of a person's face, and thelocation, size and position of the marker relative to the remainder ofthe image may reflect that the person is standing up. The analysisprocess 2430 may then anticipate that a certain region of the imagecontains pants, shorts, or even underwear. Then the apparelidentification process 2430 inspects color and/or patterns to identifyboundaries.

According to an embodiment, one result of the apparel identificationprocess 2430 is that features or characteristics of detected objects ofinterest are determined and extracted, or otherwise segmented away fromthe remainder of the image. As such, the process 2430 may be performedby one or more modules of segmentation and/or feature extraction, suchas described with an embodiment of FIG. 1. The features or shapes may beused to identify the apparel or clothing, as well as the attributes ofthe apparel or clothing. For example, the shape of a determined objectmay match a pair of pants. Additional attributes that are descriptive ofthe pants include whether the pants or baggy or tight, and the color ofthe pants.

In one embodiment, a result of the process 2430 is that (i) an apparel,item of clothing or other object of interest that is determined to becontained in the image is identified (“apparel identifier 2432”), and/or(ii) features or characteristics (e.g. color, how the item fits) of theitem are determined and extracted (“feature 2434”). In one embodiment,the identification of the apparel or its features is stored with thepage, or otherwise in association with the image. Extracted features maybe performed and correspond to any feature extraction process such asdescribed with FIG. 1 or FIG. 7-10. The apparel identifier 2432 may beclass-specific, and performed with segmentizer 110 (FIG. 1) incombination with object determinator 140 (FIG. 1).

Features 2434 may be in the form of text (“handbag, brown”), numeric orimage-based (similarity determined handbag that is highly processed).For example, in one embodiment, an image signature of the identifiedobject (i.e. clothing or apparel) is determined. The image signature maycorrespond to a vector representation of the image.

In addition or as an alternative to the image signature, one or moreembodiments provide that a similarity image is pre-associated with theidentified clothing or apparel of interest. The similarity image may beone that has been processed in a library or collection of images (e.g.e-commerce library). The similarity image may substitute for theidentified object or apparel in the event the identifiedapparel/clothing is subsequently specified for a visual search. Forexample, if the process 2430 determines that an image of a personcontains a shirt of a particular pattern, a matching or similar imagefrom a known and processed library of images is identified. If a viewerof a page subsequently wants to do a search that specifies, for example,a pair of pants worn by an athlete, the visual search may be performedon a different, but similar pair of pants, where the image used toperform the search is highly processed.

An activation process 2440 may then activate portions of select imageson the web page. The activation process may encode or embed script withthe source of the web page so that an identified object of interest froman image (e.g. pants of a picture of a person) is active. The activeportion of any image may be used for a subsequent visual search.

While an embodiment of FIG. 24 provides for a series of programmaticprocesses, one or more embodiments contemplate use of a manual processas a substitute. In one embodiment, processes of apparel identification2430, for example, may be manually performed.

As an addition or alternative embodiment, manual processes may beperformed to enrich or enhance one or more programmatic embodimentsdescribed. For example, the results of the apparel identification may bepresented to an editor for manual confirmation.

Furthermore, while embodiments described herein and elsewhere providefor searching for visual characteristics of a query item to identifyother items of the same class, an embodiment such as described with FIG.23 or FIG. 24 contemplate cross-category searching. For example, if thesearch criteria corresponds to a shirt, the visual characteristics ofthe shirt may be used to also identify one or more items of apparel thatmatch.

It is contemplated for embodiments of the invention to extend toindividual elements and concepts described herein, independently ofother concepts, ideas or system, as well as for embodiments to includecombinations of elements recited anywhere in this application. Althoughillustrative embodiments of the invention have been described in detailherein with reference to the accompanying drawings, it is to beunderstood that the invention is not limited to those preciseembodiments. As such, many modifications and variations will be apparentto practitioners skilled in this art. Accordingly, it is intended thatthe scope of the invention be defined by the following claims and theirequivalents. Furthermore, it is contemplated that a particular featuredescribed either individually or as part of an embodiment can becombined with other individually described features, or parts of otherembodiments, even if the other features and embodiments make nomentioned of the particular feature. Thus, the absence of describingcombinations should not preclude the inventor from claiming rights tosuch combinations.

What is claimed is:
 1. A computer-implemented method performed by one ormore processors and comprising: analyzing, using the one or moreprocessors, content of a web page that includes an image, whereinanalyzing the content includes: analyzing text or metadata of the webpage to determine one or more assumptions about a location or shape of afirst object in the image; performing image analysis on the image basedon the one or more assumptions to detect the first object in the image;segmenting the first object from a remainder of the image; anddetermining information about the first object, based on the imageanalysis, using one or more sources other than the content of the webpage; providing at least a portion of the first object depicted in theimage to be a selectable feature of the web page, separate from theremainder of the image, at least the portion of the first object beingassociated with the determined information; receiving data indicating aselection input corresponding to at least the portion of the firstobject; and performing a search operation to determine a set of objectsthat are deemed to be visually similar to the first object based on thedetermined information.
 2. The method of claim 1, further comprisingreceiving a trigger signal from a site of the web page, and thenaccessing the web page to retrieve the content for analyzing in responseto receiving the trigger signal.
 3. The method of claim 2, furthercomprising remotely causing the trigger signal to generate in responseto a download of the web page.
 4. The method of claim 1, whereinanalyzing the content includes determining a classification of the firstobject.
 5. The method of claim 4, wherein the classification correlatesthe first object to a person.
 6. The method of claim 1, whereinanalyzing the content includes detecting the first object from imagedata in the content of the web page.
 7. The method of claim 6, whereinanalyzing the content includes determining a signature of the firstobject from the image data.
 8. The method of claim 6, wherein analyzingthe content includes identifying one or more visual characteristics ofthe first object from the image data.
 9. The method of claim 8, whereinthe one or more visual characteristics correspond to one or more of acolor, a pattern, or a shape of at least the portion of the firstobject.
 10. The method of claim 1, wherein performing the searchoperation includes identifying a record that includes an image of asecond object that is deemed visually similar to the first object. 11.The method of claim 10, wherein performing the search operation includesusing an index.
 12. A system comprising: one or more processors; memorycoupled to the one or more processors, wherein the memory storesinstructions that, when executed by the one or more processors, causethe one or more processors to: analyze content of a web page thatincludes an image, including: analyzing text or metadata of the web pageto determine one or more assumptions about a location or shape of afirst object in the image, performing image analysis on the image basedon the one or more assumptions to detect the first object in the image,segmenting a first object from a remainder of the image, and determininginformation about the first object, based on image analysis, using oneor more sources other than the content of the web page; provide at leasta portion of the first object depicted in the image to be a selectablefeature of the web page, separate from the remainder of the image, atleast the portion of the first object being associated with thedetermined information; and receive data indicating a selection inputcorresponding to at least the portion of the first object; perform asearch operation to determine a set of objects that are deemed to bevisually similar to the first object based on the determinedinformation.
 13. The system of claim 12, wherein the one or moreprocessors are configured to analyze the image of the web page todetermine whether the first object is of a predetermined class.
 14. Thesystem of claim 13, wherein the predetermined class of the first objectcorresponds to at least one of apparel, clothing or jewelry.
 15. Thesystem of claim 13, wherein the one or more processors are configured tosegment the first object from the remainder of the image by detectingwhether the first object as depicted in the image includes one or moremarkers for the predetermined class of objects.
 16. The system of claim12, wherein the one or more processors are configured to determine asignature for the first object based on image data that comprises theimage.
 17. The system of claim 16, wherein the signature identifies atleast one of the first object as a whole, the portion of the firstobject, or a visual characteristic of the first object.
 18. The systemof claim 12, wherein the one or more processors are configured toprovide the image on the web page in an interactive form, so that a useris able to provide a selection input corresponding to the first objectin order to cause the one or more processors to perform the searchoperation.
 19. The system of claim 12, wherein the one or moreprocessors are configured to generate a search criterion for a searchengine based on the determined information in response to a userproviding the selection input.
 20. The system of claim 19, wherein theone or more processors are configured to provide a search result fromthe search engine that includes one or more images.
 21. The system ofclaim 20, wherein each of the one or more images of the search resultdepicts a merchandise item for sale, and is provided with a link to anetwork location where the merchandise item is purchasable.
 22. Thesystem of claim 12, wherein the one or more processors are configured toprovide a triggering component and a retrieval component, wherein thetriggering component resides at a location associated with the web pageand triggers the retrieval component when the content is downloaded. 23.The system of claim 12, wherein the first object corresponds to aclothing, an apparel, or a jewelry worn by a person.
 24. Acomputer-implemented method performed by one or more processors andcomprising: analyzing, using the one or more processors, content of aweb page that includes an image, wherein analyzing the content includesanalyzing text or metadata of the web page to determine one or moreassumptions about a location and shape of a person, and one or more of aclothing, apparel, or jewelry worn by the person; performing imageanalysis on the image based on the one or more assumptions to detect theperson and at least one of the clothing, apparel, or jewelry in theimage; segmenting, from the image, the person, and the detected one ormore of the clothing, apparel, or jewelry; determining information aboutthe detected one or more of the clothing, apparel, or jewelry, based onthe image analysis, using one or more sources other than the content ofthe web page; providing at least a portion of the image corresponding tothe detected one or more of the clothing, apparel, or jewelry, to be aselectable feature on the web page, separate from the person and aremainder of the image; receive data indicating a selection input from auser that corresponds to the one or more of the clothing, apparel orjewelry; and perform a search operation to determine a set of objectsthat are deemed to be visually similar to the one or more of theclothing, apparel or jewelry that corresponds to the selection input.25. The method of claim 24, wherein performing the search operationincludes using a criterion that is determined from the one or more ofthe clothing, apparel or jewelry that corresponds to the selectioninput.
 26. The method of claim 25, wherein performing the searchoperation includes performing the search operation based on a criterionthat is determined from a signature of the one or more of the clothing,apparel or jewelry that corresponds to the selection input.
 27. Themethod of claim 24, wherein analyzing the content includes detecting amarker for a face of the person depicted in the image, and whereinsegmenting one or more of the clothing, apparel, or jewelry worn by theperson depicted in the image includes processing the image using themarker of the face as a reference point.
 28. The method of claim 27,wherein processing the image using the marker of the face as thereference point includes determining a skin color of the face, anddetecting a clothing from a variation in color in the image at a regionof the image where the clothing is likely to be located in relation tothe reference point.
 29. The method of claim 24, wherein analyzing thecontent is performed automatically and in response to a page view of theweb page by the user.
 30. The method of claim 24, wherein analyzing thecontent includes performing face detection on a portion of the imagethat contains at least a part of the person.